Abstract
Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their preferences over time evolve. This paper proposes a collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model. Considering that the user's evaluation of an item will change over time, we add a time-decay function to the subject model and give its variational inference model. In the collaborative filtering score, we generate a hybrid score for similarity calculation with the topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.
Highlights
With the advent of the Internet, the availability of information continues to increase rapidly
We propose a hybrid dynamic collaborative filtering algorithm (HDCF) that can capture the evolution of topics in a collaborative filtering algorithm
Generating the HDCF Algorithm Most topic models assume that documents are interchangeable in a collection, that is, their probability is constant for permutation
Summary
With the advent of the Internet, the availability of information continues to increase rapidly This richness makes it difficult for users to effectively find the information they need in the large amount of data they can access. The number of users and items in the recommendation system is very large, users often only score a small number of items, and the user rating data are extremely sparse, which makes the nearest neighbor set obtained by the traditional similarity measurement method not accurate enough, resulting in a reduction in the recommendation quality of the algorithm. (1) The traditional CF algorithm gives the same weight to all items of interest, ignoring the influence of user time on interest when calculating the similarity. Aiming at the above problems, this paper introduces a time-decay function in the LDA model that gives different weights to items according to the time users look at them.
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