Preference Networks: Probabilistic Models for Recommendation Systems

Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-$N$ recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold Coast, Australia, pages 195--202, 200

SLOPE ONE COLLABORATIVE FILTERING BASED ON USER SIMILARITY

To solve the problem of data sparsity in traditional collab-orative filtering algorithm and improve the accuracy of rec-ommendation algorithm, a Slope based on user similarity is proposed.One Filling Score Matrix Collaborative Filtering Algo-rithms. The algorithm first uses the basic cosine similarity to calculate the similarity between users and generates the user similarity matrix, then fills the original user similarity matrix with the score predicted value of Slope One algorithm as the backfill value of the first n neighbors, and finally recommends the filled similarity matrix according to the traditional item-based collaborative filtering algorithm. Slope One collabora-tive filtering algorithm based on user similarity takes full ac-count of the similarity between users when filling the matrix, which makes the score prediction more accurate. The new al-gorithm alleviates the problem of data sparsity to a certain extent and improves the accuracy of the algorithm. The im-proved algorithm, classical collaborative filtering algorithm and even-weighted Slope One algorithm are tested on Movie Lens dataset. The results show that the Slope One collabora-tive filtering algorithm based on user similarity effectively al-leviates the problem of data sparsity and has better recom-mendation effect