Dynamics Of User Preferences Research Articles

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Graph Neural Network (GNN) is a promising technique in representation learning on graph data. Graph Convolution Network (GCN) and Graph Attention Network (GAT) are two main representative techniques in GNN, which can learn node embedding on the graph by aggregating the embedding of the neighborhoods. However, the complex computation process of existing GCN and GAT makes them less effective for tasks with complex graph structures. Moreover, traditional GCN and GAT ignore the time factor in user representation learning, so the obtained user preference model is static and cannot reflect the dynamics of user preferences. In this work, we propose a time-aware Lightweight Graph Convolutional Attention Network (LightGCAN), which can capture static and dynamic user preferences efficiently by using different GNN strategies. Specifically, static user preferences are modeled by a GCN with only neighborhood aggregation, and dynamic user preferences are extracted by a time-aware GAT based on recently interacted items. Static and dynamic user preferences are then combined and fed into a dual-channel Deep Neural Network (DNN) model for feature interaction learning and matching score prediction. Extensive experiments on different datasets show that LightGCAN is superior to the SOTA recommendation method.

Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings.

In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such context-aware user-item interactions in terms of the couplings between the user and item features and sequential user actions on items over time. However, such joint modeling is non-trivial and significantly challenges the existing work on preference modeling, which either only models user-item interactions by latent factorization models but ignores user preference dynamics or only captures sequential user action patterns without involving user/item features and context factors and their coupling and influence on user actions. We propose a neural time-aware recommendation network (TARN) with a temporal context to jointly model 1) stationary user preferences by a feature interaction network and 2) user preference dynamics by a tailored convolutional network. The feature interaction network factorizes the pairwise couplings between non-zero features of users, items, and temporal context by the inner product of their feature embeddings while alleviating data sparsity issues. In the convolutional network, we introduce a convolutional layer with multiple filter widths to capture multi-fold sequential patterns, where an attentive average pooling (AAP) obtains significant and large-span feature combinations. To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network. The experiments on typical public data sets demonstrate that TARN outperforms state-of-the-art methods and show the necessity and contribution of involving time-aware preference dynamics and explicit user/item feature couplings in modeling and interpreting evolving user preferences.

CONSTRUCTION OF PATTERNS OF USER PREFERENCES DYNAMICS FOR EXPLANATIONS IN THE RECOMMENDER SYSTEM

The subject of study in the article is the processes of constructing explanations in recommendation systems. Objectives. The goal is to develop a method of constructing patterns that reflect the dynamics of user preferences and provide an opportunity to form an explanation of the recommended list of items, taking into account changes in the user’s requirements of the recommendation system. Construction of explanations taking into account the dynamics of changes in consumer preferences makes it possible to increase user confidence in the results of the intelligent system. Tasks: structuring models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system; development of a method for constructing patterns of changing user preferences using process mining technology; experimental verification of the method for constructing patterns of changing consumer preferences. The approaches used are: temporal logics, which determine the approaches to the description of the temporal ordering of a set of events. The following results are obtained. The structuring of models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system is performed; developed and performed an experimental test of the method of constructing patterns of user preferences dynamics. Conclusions. The scientific novelty of the results is as follows. The method of dynamics patterns construction of users’ preferences for the formation of explanations concerning the recommended list of subjects is offered. The method sequentially generates a set of ordered events, each of which reflects the choice of the subject by a group of users at a certain time interval, and also builds a graph representation of the patterns of user preferences through intellectual analysis of processes. The patterns obtained as a result of the method consist of time-ordered pairs of events that reflect the knowledge of changing user preferences over time. Further use of such dependencies as elements of the knowledge base makes it possible based on probabilistic inference to build a set of alternative explanations for the received recommendation, and then arrange these explanations according to the probability of their implementation for the recommended list of subjects.

A social recommender system based on matrix factorization considering dynamics of user preferences

A social recommender system based on matrix factorization considering dynamics of user preferences

Modeling user preference dynamics with coupled tensor factorization for social media recommendation

An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

Exploiting dynamic changes from latent features to improve recommendation using temporal matrix factorization

Recommending sustainable products to the target users in a timely manner is the key drive for consumer purchases in online stores and served as the most effective means of user engagement in online services. In recent times, recommender systems are incorporated with different mechanisms, such as sliding windows or fading factors to make them adaptive to dynamic change of user preferences. Those techniques have been investigated and proved to increase recommendation accuracy despite the very volatile nature of users’ behaviors they deal with. However, the previous approaches only considered the dynamics of user preferences but ignored the dynamic change of item properties. In this paper, we present a novel Temporal Matrix Factorization method that can capture not only the common users’ behaviours and important item properties but also the change of users’ interests and the change of item properties that occur over time. Experimental results on a various real-world datasets show that our model significantly outperforms all the baseline methods.

Detailing explanations in the recommender system based on matching temporal knowledge

The problem of matching knowledge in the temporal aspect when constructing explanations for recommendations is considered. Matching allows reducing the influence of conflicting knowledge on the explanation in a recommender system. A model of knowledge representation in the form of a temporal rule with the explanation constraint is proposed. The temporal rule sets the order for two sets of events of the same type that occurred at two different time intervals in time. An explanation constraint establishes a correspondence between the temporal order represented by the rule for a pair of intervals and the description of temporal dynamics for a given time period. This dynamic is represented by the explanation of the recommendation. The model is designed to match knowledge, taking into account the explanation constraint, as well as further use the matched knowledge to clarify explanations based on the results of the intelligent system. A method for clarifying explanations in a recommender system based on knowledge matching in the form of temporal rules is developed. The method uses records of purchases of goods, services or their ratings as input data. The method identifies a subset of rules matched in the temporal aspect, which represent the same dynamics of consumer demand for the target item (increase or decrease) as explanations in the recommender system. Matching of temporal knowledge makes it possible to form a refined list of explanations. This list includes basic and clarifying explanations. The basic explanation reflects the dynamics of user interests for the entire given period of time. Clarifying explanation specifies changes in demand for individual intervals within a given time period. The use of the temporal dynamics of user preferences in the explanation is aimed at increasing confidence in the received recommendations

TSCMF: Temporal and social collective matrix factorization model for recommender systems

In real-world recommender systems, user preferences are dynamic and typically change over time. Capturing the temporal dynamics of user preferences is essential to design an efficient personalized recommender system and has recently attracted significant attention. In this paper, we consider user preferences change individually over time. Moreover, based on the intuition that social influence can affect the users’ preferences in a recommender system, we propose a Temporal and Social Collective Matrix Factorization model called TSCMF for recommendation. We jointly factorize the users’ rating information and social trust information in a collective matrix factorization framework by introducing a joint objective function. We model user dynamics into this framework by learning a transition matrix of user preferences between two successive time periods for each individual user. We present an efficient optimization algorithm based on stochastic gradient descent for solving the objective function. The experiments on a real-world dataset illustrate that the proposed model outperforms the competitive methods. Moreover, the complexity analysis demonstrates that the proposed model can be scaled up to large datasets.

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users’ preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

Collective attention patterns under controlled conditions

Increasing concerns over widespread manipulation have led to an examination of how the direct promotion of content affects user behavior. Several studies have hypothesized that promotions exert their effects through a combination of social influence and attention redirection that are presently not well understood. The particulars of the promotion mechanism itself, however, might play an important role. Here we focus on a particular type of promotion mechanism: one that promotes identical content for the entire community of users. Through a large-scale analysis of Wikipedia page-views we show that fundamentally different processes underpin the longitudinal dynamics of user attention and preferences on Wikipedia compared to other promotion systems. Whereas other platforms follow history dependent processes, we found that the effects of community-wide promotions can be modeled using a history independent process. These findings allow to effectively compare view dynamics across online services.

Dynamic attention-integrated neural network for session-based news recommendation

Online news recommendation aims to continuously select a pool of candidate articles that meet the temporal dynamics of user preferences. Most of the existing methods assume that all user-item interaction history are equally importance for recommendation, which is not alway applied in real-word scenario since the user-item interactions are sometime full of stochasticity and contingency. In addition, previous work on session-based algorithms only considers user sequence behaviors within current session without incorporating users’ historical interests or pointing out users’ main purposes within such session. In this paper, we propose a novel neural network framework, dynamic attention-integrated neural network, to tackle the problems. Specifically, we propose a dynamic neural network to model users’ dynamic interests over time in a unified framework for personalized news recommendations. News article semantic embedding, user interests modelling, session-based public behavior mining and an attention scheme that used to learn the attention score of user and item interaction within sessions are four key factors for online sequences mining and recommendation strategy. Experimental results on three real-world datasets show significant improvements over several baselines and state-of-the-art methods on session-based neural networks.

The Same Person Is Never the Same: Introducing Mood-Stimulated Thought/Action Tendencies for User-Centered Design

How people think and act is influenced by their transient mood state. Different moods stimulate different (thought/action) tendencies, such as the tendency to be attentive (when cheerful), to be cautious (when anxious), or to be impatient (when agitated). To support an understanding of how mood can inform user-centered design, this paper reports an exploratory study that revealed the diverse scope of these mood-stimulated human tendencies. The questionnaire study (N = 43) examined the relationships between 20 moods and 68 distinct tendencies. Significant mood effects were found for all tendencies, indicating that different moods are associated with different tendencies. A Correspondence Analysis generated a visual overview of these relationships. In addition, a Factor Analysis found nine generic dimensions of mood-stimulated tendencies. In user-centered design, these results can support communications about user mood with team-members, end-users and other stakeholders. Based on the study results, a creative design tool is introduced. It aims to enable designers and service providers to become better aware of, and adequately respond to, the dynamics of mood-stimulated user preferences, feelings, and actions during the design process.

On analyzing user preference dynamics with temporal social networks

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer’s benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Modeling Users Preference Dynamics and Side Information in Recommender Systems

In recommender systems user preferences can be fairly dynamic, as users tend to exploit a wide range of items and modify their tastes accordingly over time. In this paper, we model user-item interactions over time using a tensor that has time as a dimension (mode). To account for the fact that user preferences change individually, we propose a new measure of user-preference dynamics (UPD) that captures the rate with which the current preferences of each user have been shifted. UPD shows the variability in how users interact with items in recommender systems. We generate recommendations based on a tensor factorization technique, where the importance of past user preferences are weighted according to their UPD values, that is, higher UPD values downweigh more past user preferences. Additionally, we exploit users’ side data, such as demographics, which improve the accuracy of recommendations based on a coupled tensor-matrix factorization scheme. Our empirical evaluation uses two real benchmark datasets from the social media platforms Last.fm and MovieLens, containing users’ history records pertaining to listening to songs and viewing movies, respectively. We demonstrate that in both datasets, there are users with a varying level of dynamics, expressed by the UPD metric. Our experimental results show that the proposed method outperforms several baselines, by taking into account both dynamics and side data of users.

Trust Evolution: Modeling and Its Applications

Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution.

Temporal dynamics in social trust prediction

Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the corresponding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results demonstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.