1,874 research outputs found
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
BPRS: Belief Propagation Based Iterative Recommender System
In this paper we introduce the first application of the Belief Propagation
(BP) algorithm in the design of recommender systems. We formulate the
recommendation problem as an inference problem and aim to compute the marginal
probability distributions of the variables which represent the ratings to be
predicted. However, computing these marginal probability functions is
computationally prohibitive for large-scale systems. Therefore, we utilize the
BP algorithm to efficiently compute these functions. Recommendations for each
active user are then iteratively computed by probabilistic message passing. As
opposed to the previous recommender algorithms, BPRS does not require solving
the recommendation problem for all the users if it wishes to update the
recommendations for only a single active. Further, BPRS computes the
recommendations for each user with linear complexity and without requiring a
training period. Via computer simulations (using the 100K MovieLens dataset),
we verify that BPRS iteratively reduces the error in the predicted ratings of
the users until it converges. Finally, we confirm that BPRS is comparable to
the state of art methods such as Correlation-based neighborhood model (CorNgbr)
and Singular Value Decomposition (SVD) in terms of rating and precision
accuracy. Therefore, we believe that the BP-based recommendation algorithm is a
new promising approach which offers a significant advantage on scalability
while providing competitive accuracy for the recommender systems
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