Information Recommendation Method Research Based on Trust Network and Collaborative Filtering

Recommender systems are an effective solution to the information overload problem, especially in the World Wide Web where we gather vast information from anonymous people around the world. Trust enhanced recommender system to be promising to overcome the cold-start and sparsity challenges of traditional recommender system as well as improving the accuracy of the recommendations. This arise a research focus about blending of the trust information and trust level prediction to the recommendation framework. From the past decade, numerous researches were done to adapt online social network trust (simply social trust) for many web applications, including e-commerce, P2P networks, multi-agent systems, recommendation systems, and service-oriented computing. Usually, online social trust prediction can be based on two mechanisms to acquire trust value: evaluating trustee on basis of truster / truster's neighbor trust experience information (local trust), otherwise evaluating trustee on the basis of the whole social network trust experience information as reputation (global trust). Here, we leverage social science theories to develop the trust models that enable the study of online social trust evolution. In this paper, we propose a matrix factorization based trust enhanced recommendation system which properly incorporates both local trust and global trust with diffusion of the social trust multi-aspects to improve the quality of recommendations for mitigating the data sparsity and the cold-start issues. Through experiments on the Epinions data set, we show that our model outperforms its standard trust enhanced counterparts with respect to accuracy on recommender systems.

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.

To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender methods, whereas it is suffering the issue of sparse rating data that will severely degenerate the quality of recommendations. To address this issue, the article proposes a novel method, named the FTRA (Fusing Trust and Ratings), trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, i.e., the conventional rating data given by users and the social trust network among the same users. The performance of FTRA is rigorously validated by comparing it with six representative methods on a real-world dataset. The experimental results show that the FTRA outperforms all other competitors in terms of both precision and recall. More importantly, our work suggests that the strategy of augmenting sparse rating data by fusing trust networks does significantly improve the quality of conventional collaborative filtering recommendation, and its quality could be further improved by means of designing more effective integrating schemes.

Aim: The coronavirus disease 2019 (COVID-19) pandemic caused the use of social networks in the field of information acquisition and transmission to increase, whereas the validity of the information available is questionable. Because people’s trust in these networks is important, this study aimed to utilize three data mining techniques to identify the hidden rules for detecting the user trust level of social networks in the context of COVID-19. Materials and Methods: An electronic questionnaire containing 27 questions was provided to users. Out of the 12 questions selected, the final question asked about the level of user trust in social networks and was considered the target class. Based on the range in value, question 12 was divided into five classes. The relevance of the remaining 11 questions was then assessed using three decision tree-based data mining techniques. Results: The results showed that the random forest technique performed better than the other techniques. Most social network users have a moderate level of trust in information regarding COVID-19; in fact, the medium class is the most widely used target class with 60% utilization rate, which affects sensitivity and specificity. The values of these measures were much higher for this class than for the other classes. Conclusion: The educational content, both its type and the amount, regarding COVID-19 that is provided on social networks, impact on user trust. As the existence of inconsistent information has had a negative impact on user trust, a small percentage of users have high trust in these networks.

As we are familiar with the increasing number and demand for online videos on the internet has led to difficulty in the extraction of required data. The recommendation system is used to filter or to collect information according to the user's preference. Nowadays, the use of recommendation systems has become important so that we can find more relevant data from huge sources. Based on the attributes such as genres, we can recommend important videos to different users. There are different video recommendation problems such as cold start, data sparsity, etc. Here in this paper, we are solving the data sparsity problem which is considered as the biggest problem as it can give poor results. There are different recommendation techniques that are used to solve various recommendation problems such as content-based filtering, real-time recommendation system, hybrid recommender system, single network-based recommendation system, collaborative filtering. We are using a single-network recommendation system to enhance the quality of recommendation.

Nowadays, a recommendation system is an important technique in the development of electronic-commerce systems and the most popular approaches that use in many recommendation systems are a collaborative filtering algorithm. However, it still has problems such as scalability and sparse data. There are several previous methods used to deal with the weakness of collaborative filtering techniques such as a hybrid user model, but the results show their disadvantages in practical use. In this paper, we proposed a hybrid recommender system with review helpfulness features, which we have used to construct the hybrid model. In the experiment, the results of three recommendation techniques are compared: collaborative filtering based on hybrid user model, user-based collaborative filtering and our proposed method. The experimental results show that our proposed method is more efficient than other methods with the same dataset, in terms of accuracy. In addition, our proposed algorithm can decrease time consuming by constructing the hybrid model in the off-line phase and calculates the recommendation results in on-line phase.

Social online learning is gaining prominence with the emergence of social media and networks. In the presence of recommendation systems, the learner has access to several educational contents and elements proposed according to his interaction, profile, dynamics and other parameters. Our proposal revolves around an intelligent recommender system for generating recommendations in social learning environments, in particular social networks, based on the learner's activities, falling into the category of hybrid recommender systems and integrating the concept of community detection in the generation of recommendations. The proposed global approach is composed of several recommender systems: the correlation and co-occurrence based recommender system aims at measuring the degree of connectivity between learners' activities in order to generate relevant recommendations in the light of learners' implicit feedback, the community detection based recommender system through friendships. This system is an important part of our overall approach as it combines the hybrid recommendation system with the community detection based on friendship links between learners and the recommendation system based on the degree of interactivity of learners with the available learning objects. This system also consists in detecting communities, which group learners with similar preferences and orientations. The results obtained prove the potential of combining the two notions of connectivity and the integration of community detection based on several indicators to produce more relevant recommendations, while offering recommendations by group of learners by applying the concept of continuous improvement.KeywordsSocial learningRecommender systemCorrelationCo-occurrenceFriendshipInteractionCommunity detection

Each and every day, a lot of courses are published online by various mentors to help the interested people learn online. But the students who are interested may find it difficult to associate themselves with appropriate course they want as there are so many courses available, even under just a single topic. To help select the best courses for the respective candidate, here a course recommendation system is designed with the concept of hybrid recommender system. Recommendation systems such Content-Based filtering and Collaborative filtering have their own advantages and disadvantages. So, to compensate the shot comes of the respective filtering models, both the models are combined to realize a hybrid recommender system for the Course Recommendation in the Social Learning network. Here, the content-based filtering uses Lasso algorithm and collaborative filtering uses KNN algorithm, and to combine the result obtained by both the filtering methods, weighted average method is used to gain the result as a hybrid result from both the filtering models. This will prove to perform very well with great efficiency for the Course Recommendation in Social Learning Network.

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.

Background: Due to enormous data on web sites, recommending users for every item is impossible. For this problem, Recommender Systems (RS) are introduced. RS is categorized into Content-Based (CB), Collaborative Filtering (CF) and Hybrid RS. Based on these techniques, recommendations are made to the user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues, here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Methods: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this system, the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: In this study, we have compared the results of Collaborative Filtering with Cosine Similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time Weight Model with Diffusion Similarity (TAW-TIW-DS) and Tag and Time Weight Model Using Diffusion Similarity and Genetic Algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compared the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split, TAW-TIW-DS-GA showed best results as compared to the existing approach.

Background: Due to huge data in web sites, recommending users for every product is impossible. For this problem Recommender Systems (RS) are introduced. RS is categorized into Content-Based (CB), collaborative Filtering (CF) and Hybrid RS. Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging feature also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues here we have proposed Incremental clustering and Trust in Collaborative Tagging. Methods: Here we have proposed a method Collaborative Tagging (CT) with Incremental Clustering and Trust which enhances the recommendation quality by removing the issues of scalability with the help of Incremental Clustering and sparsity and cold start user or item problems are resolved with the help of Trust. Results: Here we have compared the results of Collaborative tagging with Incremental Clustering and Trust (CFT-EDIC-TS) with the baseline approaches of CT with Cosine similarity (CFT-CS), CT with Euclidian Distance and Incremental Clustering (CFT-EDIC) and CT with Trust (CFT-TS). Conclusion: Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Precision and Recall. Based on these metrics for every split CFT-EDICTS shown best results as compared to other baseline approaches.

Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSN topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncer- tainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs. Index Terms—Interpersonal trust, trust establishment, online social networks, three-valued subjective logic, Dirichlet distribu- tion

How did the economic crisis impact social capital in European societies? The empirical studies conducted so far provide contradictory conclusions about the strength and direction of its influence. We argue that to better understand the effects of the economic crisis on social capital (social trust, formal and informal networks) it is crucial to examine both its impact on people’s economic situation and the way it reshaped the relationship between individuals and political institutions and altered key political factors (political trust, the welfare state, political activism). Our analysis of European Social Survey data between 2006 and 2012 shows that changes in social trust were smaller than in formal and informal social networks. It also confirms that political factors played an important mediating role in producing these changes: changes in social trust and formal networks can especially be explained by the impact of the political factors, while variations in informal networks are mainly due to the changing economy. Moreover, the analyses show that while the economic crisis generally lowered social capital, some mechanisms such as a sense of togetherness and left-wing political activism, enhanced social capital.

The primary objective of recommendation systems (RSs) is to analyze user’s fondness and taste and recommend similar items to him/her. There exist various methods, e.g., user/item collaborative filtering (CF), content-based filtering (CBF), association rule mining (ARM), hybrid recommender system (HRS), for recommendations. Though these methods possess excellent characteristics, they are inefficient in providing good recommendations in particular situations. For example, item CF produces recommendations for cold-start objects; however, it typically has low accuracy compared to user CF. Conversely, user CF often provides more accurate recommendations; however, it fails to provide recommendations for cold-start objects. The hybrid methods aim to combine different approaches coherently to yield better recommendations. This paper presents an HRS based on user CF, item CF, and adaptive ARM. The proposed HRS employs ARM as a fundamental method; however, it considers only a set of users who are nearest to the target user to generate association rules (ARs) among items. Also, the support levels to mine associations among items are adaptive to the number of rules generated. Results of the study indicate that the proposed HRS provides more personalized and practical suggestions compared to the traditional methods.

Recommender systems have become an invaluable asset to online services with the ever-growing number of items and users. Most systems focused on recommendation accuracy, predicting likable items for each user. Such methods tend to generate popular and safe recommendations, but fail to introduce users to potentially risky, yet novel items that could help in increasing the variety of items consumed by the users. This is known as popularity bias, which is predominant in methods that adopt collaborative filtering. Recently, however, recommenders have started to improve their methods to generate lists that encompass diverse items that are both accurate and novel through specific novelty-driven algorithms or hybrid recommender systems. In this paper, we propose a recommender system that uses the concepts of Experts to find both novel and relevant recommendations. By analyzing the ratings of the users, the algorithm promotes special Experts from the user population to create novel recommendations for a target user. Thus, different users are promoted dynamically to Experts depending on who the recommendations are for. The system used data collected from Last.fm and was evaluated with several metrics. Results show that the proposed system outperforms matrix factorization methods in finding novel items and performs on par in finding simultaneously novel and relevant items. This system can also provide a means to popularity bias while preserving the advantages of collaborative filtering.