A Probabilistic Recommendation Method Inspired by Latent Dirichlet Allocation Model

Abstract

The recent decade has witnessed an increasing popularity of recommendation systems, which help users acquire relevant knowledge, commodities, and services from an overwhelming information ocean on the Internet. Latent Dirichlet Allocation (LDA), originally presented as a graphical model for text topic discovery, now has found its application in many other disciplines. In this paper, we propose an LDA-inspired probabilistic recommendation method by taking the user-item collecting behavior as a two-step process: every user first becomes a member of one latent user-group at a certain probability and each user-group will then collect various items with different probabilities. Gibbs sampling is employed to approximate all the probabilities in the two-step process. The experiment results on three real-world data sets MovieLens, Netflix, and Last.fm show that our method exhibits a competitive performance on precision, coverage, and diversity in comparison with the other four typical recommendation methods. Moreover, we present an approximate strategy to reduce the computing complexity of our method with a slight degradation of the performance.

A Hybrid Generative Model for Online User Behavior Prediction

“…We compare our hybrid generative model (HGM) 5 with the predictive multinomial mixture model (MMM) [34], which is the state-of-the-art work in item-consumption prediction. Other approaches such as Non-negative matrix factorization [23], Hierarchical Bayes Poisson factorization [42] or Latent Dirichlet Allocation [16] have also been compared in [34], and MMM outperformed previous baselines.…”

“…The results of the two models are similar across all datasets, which may be due to the user's history and popularity. Both models tend to assign high 5 https://github.com/hellpoethero/Hybrid-Generative-Model 6 We set K equals to 3 for twNYloc, 4 for goNYloc, goSFloc and twOCloc, 7 for redditS and 10 for lastfm TABLE 4. Recall@100, average rank and AUC across different data sets.…”

“…LDA is a generative statistical model which is originally used in text analysis to discover the hidden topic in the collection of documents [16]. Although LDA is introduced for text analysis, several researchers [3], [5], [17], [18] have employed this technique in recommendation tasks and achieved considerable results. Besides, this method was chosen as a basis for our model development because of two features that it demonstrates: (1) By using LDA, our model can explore new behavior, and (2) Combining LDA with other components is feasible.…”

“…Authors in [29] proposed a Twitter followee recommendation algorithm based on LDA. A recommendation model for different types of dataset employing LDA has been developed by [5].…”

“…With the growth and intense competition in e-commerce and online services, online user behavior prediction has become an active research field. Various techniques have been applied to tackle this problem, such as Matrix factorization [1], [2], Latent Dirichlet Allocation [3]- [5], Markov model [6]- [9], Recurrent neural network [10]- [12] and so on.…”