Review-Based Topic Distribution Profile for Recommender Systems

A recommendation system for scientific water data

“…Also, they show that distance metrics based upon probability distribution, such as Jensen-Shannon distance [10,13,32], are reasonable to use for calculating similarities of probability distributions. Saraswat et al [39] builds a topic preference profile for each user based upon each document's topic probability distribution generated by the LDA model with a weight for each document from the user's review rating. Rosen-Zvi et al [38] extends the basic LDA model to an author-topic model, which solves the problem of a document with multiple authors and summarizes each author's interests of topics.…”

“…We compared the performance of our CBF LDA method with several other recommendation algorithms, including making recommendations based upon training an original LDA model on the entire resources space [22,23], building a user's preference profile as a topic-distribution vector based upon the LDA model trained from resources selected by each user [39], two model-based collaborative filtering algorithms on users' implicit feedback: the alternating least-squares model on implicit feedback [18,33] and Bayesian personalized ranking methods [36], and the recurrent neural networks method for session-based recommendations [16]. We perform each testing task on the same dataset with the same evaluation method.…”

“…We compared our algorithm with a modified version of the method proposed by Saraswat et al [39], which uses the LDA model to build a topic-distribution profile for each user.…”

“…In our implementation, instead of weighting each document by the rating explicitly provided by the user as described in Saraswat et al [39], we assign a weight to each keyword based upon the user's browsing of resources. First, we apply users' 30-day activity logs to build a user-resource relation matrix M U R with each row corresponds to a user and each column represents a resource.…”

“…For each user u, first, we train an LDA model based upon resources selected by the user, which is the same training process as we have for our CBF LDA approach. Then, given the topic-word distribution Φ u from the user u's LDA model, Φ u (z, k) represents the topic-word probability of topic z for keyword k. As in Saraswat et al [39], we represent user u's interest for topic z as P(u, z), which is defined as:…”