skip to main content

Title: Sequential Recommendation with User Memory Networks
User preferences are usually dynamic in real-world recommender systems, and a user’s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms – including both shallow and deep approaches – usually embed a user’s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user’s historical records and future interests. In this paper, we aim to express, store, and manipulate users’ historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users’ historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users’ sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses more » show that our method is able to extract the intuitive patterns of how users’ future actions are affected by previous behaviors. « less
; ; ; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
Web Search and Data Mining
Sponsoring Org:
National Science Foundation
More Like this
  1. With the increase in volume of daily online news items, it is more and more difficult for readers to identify news articles relevant to their interests. Thus, effective recommendation systems are critical for an effective user news consumption experience. Existing news recommendation methods usually rely on the news click history to model user interest. However, there are other signals about user behaviors, such as user commenting activity, which have not been used before. We propose a recommendation algorithm that predicts articles a user may be interested in, given her historical sequential commenting behavior on news articles. We show that following this sequential user behavior the news recommendation problem falls into in the class of session-based recommendation. The techniques in this class seek to model users' sequential and temporal behaviors. While we seek to follow the general directions in this space, we face unique challenges specific to news in modeling temporal dynamics, e.g., users' interests shift over time, users comment irregularly on articles, and articles are perishable items with limited lifespans. We propose a recency-regularized neural attentive framework for session-based news recommendation. The proposed method is able to capture the temporal dynamics of both users and news articles, while maintaining interpretability.more »We design a lag-aware attention and a recency regularization to model the time effect of news articles and comments. We conduct extensive empirical studies on 3 real-world news datasets to demonstrate the effectiveness of our method.« less
  2. The success of cross-domain recommender systems in capturing user interests across multiple domains has recently brought much attention to them. These recommender systems aim to improve the quality of suggestions and defy the cold-start problem by transferring information from one (or more) source domain(s) to a target domain. However, most cross-domain recommenders ignore the sequential information in user history. They only rely on an aggregate or snapshot of user feedback in the past. Most importantly, they do not explicitly model how users transition from one domain to another domain as users continue to interact with different item domains. In this paper, we argue that between-domain transitions in user sequences are useful in improving recommendation quality, dealing with the cold-start problem, and revealing interesting aspects of how user interests transform from one domain to another. We propose TransCrossCF, transition-based cross-domain collaborative filtering, that can capture both within and between domain transitions of user feedback sequences while understanding the relationship between different item types in different domains. Specifically, we model each purchase of a user as a transition from his/her previous item to the next one, under the effect of item domains and user preferences. Our intensive experiments demonstrate that TransCrossCF outperformsmore »the state-of-the-art methods in recommendation task on three real-world datasets, both in the cold-start and hot-start scenarios. Moreover, according to our context analysis evaluations, the between-domain relations captured by TransCrossCF are interpretable and intuitive.« less
  3. Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users’ social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at:
  4. Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling, and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario. Through careful analysis of real-world datasets, we find that personalized item frequency (PIF) information (which records the number of times that each item is purchased by a user) provides two critical signals for NBR. But, this has been largely ignored by existing methods. Even though existing methods such as RNN based methods have strong representation ability, our empirical results show that they fail to learn and capture PIF. As a result, existing methods cannot fully exploit the critical signals contained in PIF. Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these criticalmore »signals. We evaluate our method on four public real-world datasets. Despite its relative simplicity, our method frequently outperforms the state-of-the-art NBR methods - including deep learning based methods using RNNs - when patterns associated with PIF play an important role in the data.« less
  5. Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are missing not at random (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn users’ negative preferences. Recent studies modeled exposure, a latent missingness variable which indicates whether an item is exposed to a user, to give each missing entry a confidence of being negative feedback. However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be an essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named “user intent” to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporate it into matrix factorization (MF) for recommendation. We also explore two types of constraints to achieve a more compact and interpretable representation of user intents. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.