Personalized advertisement system using social relationship based user modeling

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Collaborative filtering (CF) recommender systems have been extensively developed and widely deployed in various social websites, promoting products or services to the users of interest. Meanwhile, work has been attempted at poisoning attacks to CF recommender systems for distorting the recommend results to reap commercial or personal gains stealthily. While existing poisoning attacks have demonstrated their effectiveness with the offline social datasets, they are impractical when applied to the real setting on online social websites. This paper develops a novel and practical poisoning attack solution toward the CF recommender systems without knowing involved specific algorithms nor historical social data information a priori. Instead of directly attacking the unknown recommender systems, our solution performs certain operations on the social websites to collect a set of sampling data for use in constructing a surrogate model for deeply learning the inherent recommendation patterns. This surrogate model can estimate the item proximities, learned by the recommender systems. By attacking the surrogate model, the corresponding solutions (for availability and target attacks) can be directly migrated to attack the original recommender systems. Extensive experiments validate the generated surrogate model's reproductive capability and demonstrate the effectiveness of our attack upon various CF recommender algorithms.

Social Tagging Systems (STS) are very popular web application such that millions of people join the systems and actively share their contents. These enormous number of users are flooding STS with contents and tags in an unrestrained way in that threatening the capability of the system for relevant content retrieval and information sharing. Recommender Systems (RS) is a known successful method for overcome information overload problem by filtering the relevant contents over the nonrelevant contents. Besides manage folksonomy information, STS also handle social network information of its users. Both information can be used by RS to generate a good recommendation for its users. This work proposes an enhanced method for an existing hybrid recommender system, by incorporating social network information into the input of the hybrid recommender. The recommendation generation process includes Random Walk with Restart (RWR) alongside Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods. Some parameters were introduced in the system to control weight contribution of each method. A comprehensive experiment with a set of a real-world open data set in two areas, social bookmark (Delicious.com) and music sharing (Last.fm) to test the proposed hybrid recommender system. The outcomes exhibit that it can give improvement compared to an existing method in terms of accuracy. The proposed hybrid achieves 24.4% more than RWR on the Delicious dataset, and 53.85% more than CBF on Lastfm dataset.

Collaborative filtering (CF), the most successful information filtering technique for recommender systems, is either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. Moreover, the similarity functions used by most recommender systems are compensatory and allow very high (pros) and very low (cons) scores to compensate each other. This paper presents a hybrid movie recommender system that retains memory-based CF accuracy, model-based CF scalability, and alleviates the compensation problem of similarity functions. The proposed recommender system relies on a compact user model and fuzzy concordance/discordance principle. The user model speeds up the online process of generating a set of like-minded users within which a memory-based CF is carried out. The inter users comparison is done by using fuzzy concordance/discordance principle to alleviate the similarity compensation problem. The pros and cons between users are measured separately and then the overall statement about them is obtained by balancing the pros and cons within the set of criteria. Besides our approach is fast and compact, computational results reveal that it outperforms the classical one.

Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sparsity and cold start problems in recommendation systems. More precisely, we construct a directed graph whose nodes consist of items and users, together with item content, user profile and social network information. We incorporate user's ratings into edge settings in the graph model. The model provides personalized recommendations and predictions to individuals and groups. The proposed algorithms are evaluated on MovieLens and Epinions datasets. Experimental results show that the proposed methods perform well compared with other graph-based methods, especially in the cold start case.

Self-image is a description or assessment of each individual's self, both in terms of knowledge and physical aspects. Meanwhile, social relationships are relationships between people who need each other and have a reciprocal relationship. The research objective is to determine the relationship between self-image and social relationships between peers on the level of self-confidence at SMAN 1 Cilaku-Cianjur. This type of research uses a quantitative descriptive research design with a cross sectional approach. Population and sample of students in grades 10 and 11. The sampling technique is: simple random sampling. The results and discussion concluded: 1. The results of the research found that there is an influence of Self Image. 2. The results of the research found that the influence of social relationships was that most of the respondents, 29 (61.7%) had moderate social relationships and some of the respondents, 18 (38.3%). ) have high emotional regulation skills in social relations. 3. The results of the study found that the self-confidence of the majority of respondents as many as 43 (91.5%) had moderate self-confidence and some respondents as many as 3 (6.4%) had high self-confidence and 1 (2.1%) of the respondents had low self-confidence. 4. There is an influence of self-image on self-confidence among teenagers at SMAN 1 Cilaku. 5. There is an influence of social relationships on self-confidence among teenagers at SMAN 1 Cilaku. 6 .Based on the results of research that has been carried out, the results of this hypothesis are accepted as showing that there is a relationship between self-image (X1) and social relationships (X2) on self-confidence (Y).

Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based on this model not only holds the advantage of recommendation accuracy in memory-based method, but also has the scalability as good as model-based method. The user model is constructed based on item combination feature and demographic information, and it focuses on searching for set of neighboring users shared with same interest, which helps to improve system scalability. To enhance recommendation accuracy, each feature in user model is given a different weight when computing the similarity between users. Genetic algorithm is adopted to learn the weight values of features. A comparison experiment was performed on MovieLens data set, and the result shows methodology proposed in this paper performs better than conventional collaborative filtering in recommendation accuracy and scalability.

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures — K-d tree and Quadtree. We cluster or partition the users’ space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or similar items are not available in the rating matrix, due to the data sparsity problem. Further, these techniques suffer from correlation based problems inherent in used similarity measures. Consequently, higher prediction accuracy cannot be achieved. In this paper, we propose the use of fuzzy Naive Bayesian (FNB) classifier for user based CF and item based CF for implicitly computing similarity between users as well as items on the basis of conditional probabilities and develop fuzzy Naive Bayesian classifier to user based CF (FNB-UB-CF) and item based CF (FNB-IB-CF). We further develop a hybrid RS (FNB-UB-IB-CF) by combining the proposed FNB-UB-CF and FNB-IB-CF. Their combinations would be helpful in alleviating the sparsity because both user ratings and item ratings are employed. Experimental results demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendation quality using a popular MovieLens dataset.

Welcome to the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016) in Halifax, Canada, July 13-16, 2016. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users. UMAP is the successor of the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It has traditionally been organized under the auspices of User Modeling Inc. This year (2016) UMAP became an ACM conference, sponsored by ACM SIG CHI and SIG WEB. The conference spans a wide scope of topics related to user modeling, adaptation, and personalization. UMAP 2016 is focused on bringing together cutting-edge research from user interaction and modeling, adaptive technologies, and delivery platforms. It includes high-quality peer-reviewed papers featuring substantive new research in one of five research areas, each chaired by leaders in the field: User Modeling for Recommender Systems (chairs: Alexander Felfernig & Pasquale Lops)Adaptive & Personalized Educational Systems (chairs: Antonija Mitrovic & Kalina Yacef)Personalization in the Social Web & Crowdsourcing Era (chairs: Alessandro Bozzon & Harith Alani)Adaptive, Intelligent, & Multimodal User Interfaces (chairs: Julien Epps & Hatice Gunes)Architectures, Techniques, & Methodologies for UMAP (chairs: Stephan Weibelzahl & Mihaela Cocea) This year we received 123 submissions. In keeping with UMAPs rigorous standards, each paper was carefully reviewed by members of the Program Committee (PC) while the Area Chairs (ACs) coordinated the reviews and provided recommendations to the Program Chairs. The international Program Committee (PC) consisted of 132 members who were assisted by 49 subreviewers. These were leading researchers as well as highly promising young researchers. Papers were assigned to at least 4 members of the PC and to 1 AC member based on their expertise, interests, and other factors. Each paper received at least 3 reviews, and 95% received 4 reviews. After the initial reviews were submitted, the designated AC facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The AC then provided a summative meta-review and a recommendation to the Program Chairs. The final decisions were based on these recommendations, the meta-reviews, and reviewer scores. We accepted 21 long papers (23.9% acceptance rate) and 13 short papers (27.6% acceptance rate) for oral presentation and an additional 17 extended abstracts for poster presentation and inclusion in the proceedings. The program also features posters, demos, and late breaking results, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization. We also invited three distinguished keynote speakers, each illustrating significant issues and prospective directions for the field. Hossein Derakhshan (shared keynote speaker with Hypertext 2016) is an Iranian-Canadian blogger who was imprisoned in Tehran from November 2008 to November 2014. He has been called the "father of Persian blogging" and has helped promote podcasting in Iran. His talk, "Killing the Hyperlink, Killing the Web: the Shift from Library-Internet to Television-Internet," reflects his views on the Internet today.Lada Adamic leads the Product Science group within Facebook's Data Science Team. She is also an adjunct associate professor at the University of Michigan's School of Information and Center for the Study of Complex Systems. Her talk "The Life and Times of Information in Networks" focuses on cascades of information-sharing and resharing within social media.Sandra Carberry was one of the founders of the User Modeling research area at the first workshop in Maria Laach in 1986. As appropriate for the 30th anniversary, her talk "User Modeling: The Past, The Present and The Future" discusses how the field evolved, insights into where the field is headed, and the hottest topics for exploration. The conference includes a doctoral consortium that provides an opportunity for doctoral students to explore and develop their research interests under the guidance of distinguished scholars. This track received 18 submissions of which nine were accepted as full papers and eight as posters. A set of seven workshops and two tutorials round out the program. (Workshop) IFUP: Workshop on Multi-dimension Information Fusion for Modeling and Personalisation (half-day) organized by Robin Burke (DePaul University, USA), Feida Zhu (Singapore Management University, Singapore), Neil Yorke-Smith (American University of Beirut, Lebanon), and Guibing Guo (Northeastern University, China)(Workshop) INRA: News Recommendation and Analytics (half-day) organized by Jon Atle Gulla (Norwegian University of Science and Technology Trondheim, Norway), Luc Martens (Minds-UGent-WiCa Ghent, Belgium), Özlem Özgöbek (Norwegian University of Science and Technology Trondheim, Norway), and Nafiseh Shabib (TNS Gallup, Oslo, Norway)(Workshop) SOAP: Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (half-day) organized by Peter Knees (Johannes Kepler University Linz, Austria), Kristina Andersen (Studio for Electro Instrumental Music, Amsterdam, the Netherlands), Alan Said (Recorded Future, Gothenburg, Sweden), and Marko Tkalcic (Free University of Bozen-Bolzano, Italy)(Workshop) HAAPIE: Human Aspects in Adaptive and Personalised Interactive Environments (half-day) organized by Panagiotis Germanakos (SAP SE, Germany), Marios Belk (Department of Computer Science, University of Cyprus), George Samaras (Department of Computer Science, University of Cyprus), and Vania Dimitrova (University of Leeds, UK)(Workshop) EvalUMAP: Towards comparative evaluation in the user modelling, adaptation and personalization space (full-day) organized by Owen Conlan (Trinity College Dublin, Ireland), Liadh Kelly (Trinity College Dublin, Ireland), Kevin Koidl (Trinity College Dublin, Ireland), Séamus Lawless (Trinity College Dublin, Ireland), Killian Levacher (Trinity College Dublin, Ireland), and Athanasios Staikopoulos (Trinity College Dublin, Ireland)(Workshop) FuturePD: The future of personal data: envisioning new personalized services enabled by Quantified Self technologies (half-day) organized by Amon Rapp (University of Torino, Italy), Federica Cena (University of Torino, Italy), Judy Kay (University of Sidney, Australia), Bob Kummerfeld (University of Sydney, Australia), Frank Hopfgartner (University Gardens Glasgow, UK), Jakob Eg Larsen (Technical University of Denmark, Denmark), and Elise van den Hoven (University of Technology Sydney, Australia). (Workshop) PALE: Personalization Approaches in Learning Environments (full-day) organized by Milos Kravcik (RWTH Aachen University, Germany), Olga C. Santos (UNED, Spain), Jesus G. Boticario (UNED, Spain), and Maria Bielikova (FIIT STUBA, Slovakia)(Tutorial) Semantics-Aware Techniques for Social Media Analysis, User Modeling, and Recommender Systems (half-day) by Pasquale Lops and Cataldo Musto (University of Bari Aldo Moro, Italy)(Tutorial) Games, Gamification and Personalization (half-day) by Amon Rapp (University of Torino, Italy).

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Recommender Systems (RSs) are playing an important role in improving user satisfaction as they can recommend items which might be highly interested to users. In recent years, it has been observed that social relations including factors such as trust and distrust among users are very useful in improving recommendation accuracy. However, traditional recommendation methods like Collaborative Filtering (CF) usually neglect social relations and even for those methods which considered social relations often fail to uncover different types of positive and negative social relations, which hinders the improvement of recommendation accuracy. To solve such a problem, in this paper, we first divide user trust relations into four fine-grained types, including strong trust, weak trust, strong distrust and weak distrust, which help to thoroughly exploit the trust and distrust relations among users. Afterwards, we propose a trust prediction framework based on a SVD algorithm to obtain weighted social relations. Finally, we employ two examples on rating prediction to demonstrate how to use fine-grained user trust relations. Experimental results based on Extended Epinions dataset show that our proposed approach based on fine-grained user trust relations can achieve better accuracy than other conventional approaches.

The cold-start problem faced by recommender systems is a serious problem, mainly because of the lack of historical data for new users or items. Traditional recommendation techniques, such as collaborative filtering and content-based filtering, are prone to fail in making good recommendations under such conditions. This paper explores the use of reinforcement learning (RL) as a remedy for cold-start problems based on active learning methods and multi-armed bandit models. We propose a novel RL-based approach that learns user preferences incrementally from interaction and improves recommendations in an exploration-exploitation setting. The setting shows improved performance in personalization and user engagement compared to baseline methods. This paper also provides the first-order implications of the cold-start problem in the real world and assesses the challenges faced by industries impacted by this problem. Keywords - Recommender systems, cold-start problem, reinforcement learning, Markov decision processes, deep reinforcement learning, user modelling

A new similarity measure for collaborative filtering based recommender systems

The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data.