Merging trust in collaborative filtering to alleviate data sparsity and cold start (original) (raw)
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Combining trust in collaborative filtering to mitigate data sparsity and cold-start problems
2014
Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it suffers from several inherent deficiencies such as data sparsity and cold start. To better show user preferences for the cold users additional information (e.g., trust) is often applied. We describe the stages based on which the ratings of an active user's trusted neighbors are incorporated to complement and represent the preferences of the active user. First, by discriminating between different users, we calculate the significance of each user to make recommendations. Then the trusted neighbors of the active user are identified and aggregated. Hence, a new rating profile can be formed to represent the preferences of the active user. In the next stage, similar users probed based on the new rating profile. Finally, recommendations are generated in the same way as the conventional CF with the difference that if a similar neighbor had not rated the target item, we will predict the value of the target item for this similar neighbor by using the ratings of her directly trusted neighbors and applying MoleTrust algorithm, so as to incorporate more similar users to generate prediction for this target item. Experimental results demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.
Collaborative filtering-based recommender systems by effective trust
International Journal of Data Science and Analytics
Collaborative filtering (CF) is one of the most well-known and commonly used techniques to build recommender systems and generate recommendations. However, it suffers from several inherent issues such as data sparsity and cold start. This paper tends to describe the steps based on which the ratings of an active users trusted neighbors are combined to complement and represent the preferences to the active user. First, by discriminating between different users, we calculate the significance of each user to make recommendations. Then, the trusted neighbors of the active user are identified and aggregated. Hence, a new rating profile can be established to represent the preferences of the active user. In the next step, similar users probed based on the new rating profile. Finally, recommendations are generated in the same way as the conventional CF with the difference that if a similar neighbor had not rated the target item, we will predict the value of the target item for this similar neighbor by using the ratings of her directly trusted neighbors and applying MoleTrust algorithm, to combine more similar users to generate a prediction for this target item. Experimental results demonstrate that our method outperforms other counterparts both in terms of accuracy and in terms of coverage.
Alleviating the sparsity problem of collaborative filtering using trust inferences
2005
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as "similar" according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method.
A Trust-Based Collaborative Filtering Approach to Design Recommender Systems
International Journal of Advanced Computer Science and Applications
Collaborative Filtering (CF) is one of the most frequently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction. Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance. Moreover, CF is fragile and it is not suitable to find similar users. The existing literatures on CF show that integrating users' social information with a recommender system can handle the above-mentioned issues effectively. Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items. In this paper, we propose a trust-based recommender system, TrustRER, which integrates users' trusts into an existing user-based CF algorithm for rating prediction. It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores. For trust score, we have defined three novel trust statements based on user rating values, emotion values, and review helpfulness votes. To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected. The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method. TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.
cegon technologies, 2019
We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques. EXISTING SYSTEM: Many approaches have been proposed in this field, including both memory-and model-based methods. Golbeck proposes a TidalTrust approach to aggregate the ratings of trusted neighbors for a rating prediction, where trust is computed in a breadth-first manner. Guo et al. complement a user's rating profile by merging those of trusted users through which better recommendations can be generated, and the cold start and data sparsity problems can be better handled. However, memory-based approaches have difficulty in
A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation
International journal of engineering. Transactions C: Aspects, 2020
Today, the trust has turned into one of the most beneficial solutions to improve recommender systems, especially in the collaborative filtering methods. However, trust statements suffer from a number of shortcomings, including the trust statements sparsity, users' inability to express explicit trust for other users in most of the existing applications. To overcome these problems, this work presents a method for computing implicit trust based on user ratings, in which four influential factors including Similarity, Confidence, Analogous Opinion, and Distance are utilized to achieve trust. For computing users' similarity, Person Correlation Coefficient measure was applied. Confidence was computed through users' common in rated items. To compute users' analogous opinions, their ratings were evaluated from three aspects of their satisfaction, dissatisfaction, and indifference about the items. Euclidean distance was employed on users ratings for computing the distance. Finally, the factors were combined to reach the implicit trust. Moreover, fuzzy c-means clustering was applied to initially partition similar users for enhancing the performance positively. Finally, two MovieLens datasets of 100K and 1M have been used to evaluate this approach, and results have shown that the approach significantly increases Accuracy, Precision and Recall, compared to some other existing methods.
cegon technologies, 2019
We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques. PROJECT OUTPUT VIDEO: (Click the below link to see the project output video): EXISTING SYSTEM: Many approaches have been proposed in this field, including both memory-and model-based methods. Golbeck proposes a TidalTrust approach to aggregate the ratings of trusted neighbors for a rating prediction, where trust is computed in a breadth-first manner. Guo et al. complement a user's rating profile by merging those of trusted users through which better recommendations can be generated, and the cold start and data sparsity
Improving Recommender Systems by Incorporating Similarity, Trust and Reputation
2014
Recommender systems using traditional collaborative filtering suffer from some significant weaknesses, such as data sparseness and scalability. In this study, we propose a method that can improve the recommender systems by combining similarity, trust and reputation. We modify the way that neighbors are selected by introducing the trust and reputation metrics in order to develop new relations between users so that it can increase the connectivity and alleviate the data sparseness problem. Throughout our 2 different scenarios of experiment simulations conducted on MovieLens dataset and the comparison of our results with other trust-based collaborative filtering research, we found out that our proposed method outperforms for better recommendations in an effective way.
A trust-aware collaborative filtering system based on weighted items for social tagging systems
2014 Iranian Conference on Intelligent Systems (ICIS), 2014
Collaborative Filtering systems consider users' social environment to predict what each user may like to visit in a social network i.e. they collect and analyze a large amount of information on users' behavior, activities or preferences and then predict or make suggestions to users. These systems use ranks or tags each user assign to different resources to make predictions. Lately, social tagging systems, in which users can insert new contents, tag, organize, share and search for contents, are becoming more popular. These social tagging systems have a lot of valuable information, but the data expansion in them is very fast and this has led to the need for recommender systems that will predict what each user may like or need make these suggestions to them. One of the problems in these systems is: "how much can we rely on the similar users, are they trustworthy?". In this article we use trust metric, which we conclude from users' tagging behavior, beside similarities to give suggestions. Results show considering trust in a collaborative system can lead to better performance in generating suggestions.