Recommender System Employing Personal-Value-Based User Model

This paper proposes user and item modeling methods towards recommender systems based on personal values. Marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. While existing recommender systems usually employ user preference of items to make recommendations, proposed method focuses on users' personal values, which mean value judgments that show what attributes users put a high priority. The influence of each attribute on item evaluation is determined based on correspondence between ratings for item and the attribute. The proposed method is applied to actual review data, of which results supports the assumption that different users put high priorities on different attributes.

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

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).

Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user's preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user's preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.

Due to the dynamic and heterogeneous nature of the Web, it is increasingly difficult for users to choose amongst large amounts of data. For this reason, the modelling of users and access to personalised information becomes essential. Recommendation systems stand out as systems that aim to provide the most appropriate and efficient services by offering personalised recommendations to users. Traditional recommendation systems provide suggestions to their users with static approaches and do not include user preferences that change over time in suggestion strategies. In this study, a comprehensive review and comparison of recommendation systems that can adaptively develop suggestion approaches according to changing user preferences and learn user preferences are presented.KeywordsCollaborative filteringClassification techniqueRecommendation systemSocial mediaUser preferences

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight `1' is given to those who have connections directly, and weight `0' is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User's types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.

With the wide spread of movie platforms and the diversity in the quantity and quality of movies shown, it is difficult for Movie audiences to find related information about the movies that they may be interested in. And have contributed to finding a way to provide a list of movies that suits the user’s desire and personalization. In this study, we present an enhancement to the recommendation system by exploiting the personalized PageRank algorithm to find the most personalized candidate list of movies for the target user. PageRank Algorithm is a useful method for the recommendation task. PageRank algorithm is used to improve the representation of movies and users in the graph network in addition to ranking movies for a target user in the recommendation activity. User preferences have been calculated based on movie genres as a part of building the user profile from his history of rating (content analysis), and for each user, this preference was used to produce a dynamic initial rank value for each movie instead of the traditional static initial rank value. In addition to personalizing the target user, a new way has been presented to personalize each user with different weights according to their rating on movies, by supporting the proposed recommender system with new personalization parameters for each user. To evaluate the performance of the proposed recommender system and measure the accuracy of recommendations, precision, and recall metrics have been used. The experimental results show that there is a significant improvement in the recommendations process, which means the hybrid recommender system using personalized PageRank (HRS-PPR) is better than the traditional recommender system using personalized PageRank. The experimental results show that the proposed approach enhances the accuracy of the recommendation regarding precision and recall measures.

Each study program requires students to take several elective courses. The appropriateness of the elective courses taken with the student's abilities can be one of the factors for the success of student studies. This research focuses on building a content-based filtering recommendation system that provides several elective courses recommendation according to the student's academic history. The proposed recommendation systems' results are based on preprocessed word items from courses taken by the user. The weighted cosine similarity between the elective courses syllabus and the user profiles is calculated. Moreover, the experiment employed a dataset of the CSUGM course syllabus. The proposed recommendation system is evaluated in two ways, i.e., questionnaire method and validation method. The questionnaire method obtains an assessment of system performance, hence the validation method to get the average accuracy. The questionnaire was conducted by involving thirty students of the CSUGM undergraduate program. The experimental results show that the proposed recommendation system has a good performance proven by the percentage of recommendation diversity 81.67%. Furthermore, the accuracy of the proposed recommendation system has an average of 64%.

Most web-based educational systems contain some drawbacks, as compared to traditional classrooms. Particularly, it becomes difficult for teachers to guide students to choose an appropriate learning resource due to the large number of online learning resources. Meanwhile, student decisions make it more difficult to choose educational resources according to their circumstances. In this matter, the resource recommender system can be employed as an educational environment to recommend the educational resource advice for students, so that these recommendations can be coordinated to each student’s preferences and needs. This paper presents the resource recommender system as a combination of MLP, BiLSTM, and LSTM improved deep learning networks using the attention method. Compared to similar studies conducted using DBN networks and focus only on the near past interests and preferences of users, the proposed system provides higher accuracy and more appropriate recommendations considering current interests, in addition to the user’s long-term past interests. The proposed recommender system with accuracy of 0.96 and a loss of 0.0822 contains a better performance to recommend resources to students compared to other methods.

Today, advertisement has become a part of media content and it is exposed to viewers in ways such as home shopping, PPL (Product Placement), and T-commerce. Even though media contents and platforms have been developed into user-interactive services, the interaction between advertisement systems and users is still limited. For advertising services to interact with users, a recommendation system should be introduced. Recommendation systems have been studied such as content-based, collaborative filtering, and hybrid methods. Since conventional recommendation systems require high computational complexity in the preference prediction process and do not consider the time factor, there are limitations in performing recommendation considering user preference. In this paper, we propose an advertisement recommendation system that predicts the ATU’s (Active Target User) preference for items in real-time. The proposed system creates PRETREE by modeling ATU's history data with a tree data structure for reducing computational complexity. A recommendation is triggered when the item that the ATU is looking at in the media content is detected. If there are rating records for the item in the history of the ATU, the preferences are predicted on the basis of PRETREE. If there is no history for the item, the proposed system predicts preference on the basis of similar groups and the LCC (Longest Common Category) node at the ATU's PRETREE. Finally, we evaluate the performance of the proposed recommendation system using real- world data.

The practice and method of collaboratively creating and managing tags to annotate and categorize content has resulted in the creation of folksonomy. Folksonomies provide new opportunities and challenges in the field of recommender systems. Despite the considerable amount of researches done in the context of recommender systems, the specific problem of integrating tags into standard recommender system algorithms is less explored than the problem of recommending tags. Collaborative filtering is one of the popular approaches for providing recommendation. However, despite the popularity of collaborative filtering, to some extent, it could not recognize the preferences of users in cold-start scenarios, where insufficient preferences are associated to certain users or items, which leads to degraded recommendation quality. This study presents a collaborative filtering approach based on the expansion of users’ tags. In this case, semantics between tags can be unveiled which subsequently resulted in the identification of semantically similar users. Experiment on real-life dataset shows that our approach outperforms the state-of-the-art tag-aware collaborative filtering approaches in terms of recommendation quality particularly in the cold-start situation.

The incompleteness and uncertainty about the state of the world and about the consequences of actions are unavoidable. If we want to predict the performance of multiuser computing systems, we have the uncertainty of what the users are going to do, and how that affects system performance. Intelligent interface agent development is one way to mitigate the uncertainty about user behaviors by predicting what users will do based on learned users' behaviors, preferences, and intentions. This work focuses on developing user models that can analyze and predict user behavior in multi-agent systems. We have developed a formal theory of user behavior prediction based on hidden Markov models. This work learns the user model through a time-series action analysis and abstraction by taking users' preferences and intentions into account in order to formally define user modeling.

The main contribution of this work is the comparison of three user modeling strategies based on job titles, educational fields and skills in LinkedIn profiles, for personalized MOOC recommendations in a cold start situation. Results show that the skill-based user modeling strategy performs best, followed by the job- and edu-based strategies.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.