Changes In User Interests Research Articles

Internet Media During the COVID-19 Crisis

This article defines the main characteristics of the media during quarantine, analyzes the peculiarity of online media during the crisis caused by the coronavirus pandemic COVID-19 and highlights changes in the content of media platforms during the spread of the coronavirus pandemic, considering changes in user interests and new media projects offered especially during this pandemic. General scientific research methods (analysis, synthesis) and special research methods of online media characteristics (content analysis) have been used. Based on the results of the survey, the most popular online platforms were identified during quarantine restrictions, the most vulnerable areas of online media were identified during the crisis, and details of the presentation of material on OVID-19 were analyzed by various means. It is concluded that social quarantine restrictions have led to an increase in activity among users of online media, now focusing on the content of the information and the constant monitoring of the situation of the pandemic, as well as ways to defend against it.

Dynamic user modeling for expert recommendation in community question answering

Community Question Answering (CQA) websites has become an important channel for people to acquire knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, i.e., expert recommendation. However, a lot of existing methods consider the expert recommendation problem in a static context, ignoring that the real-world CQA websites are dynamic, with users’ interest and expertise changing over time. Although some methods that utilize time information have been proposed, their performance improvement can be limited due to fact that they fail they fail to consider the dynamic change of both user interests and expertise. To solve these problems, we propose a deep learning based framework for expert recommendation to exploit user interest and expertise in a dynamic environment. For user interest, we leverage Long Short-Term Memory (LSTM) to model user’s short-term interest so as to capture the dynamic change of users’ interests. For user expertise, we design user expertise network, which leverages feedback on users’ historical behavior to estimate their expertise on new question. We propose two methods in user expertise network according to whether the dynamic property of expertise is considered. The experimental results on a large-scale dataset from a real-world CQA site demonstrate the superior performance of our method.

Step-Enhancement of Memory Retention for User Interest Prediction

User behavior modeling and interest prediction are always the key elements in preference analysis, product recommendation and personalized service. The psychological memory theory has been proved capable of reflecting changes in user interest. However, merely focusing on the memory forgetting mechanism (the retention-only model) or considering the superimposition of interest retention in short term (the gradual-retention model), existing methods have a poor prediction capability because of ignoring the long-term impact of repeated behaviors. In this paper, we propose a step-enhancement of memory retention (SEMR) model which integrates the cross-enhancement-effects of multiple historical behaviors under different time windows to characterize user interest. In addition, we use some extended correction methods to eliminate the effect of discontinuous records. Numerical experiments using real TV viewing data validate the efficiency of our proposed model and methods, which reduce the average prediction error to 0.3, outperforming the traditional models by around 50%.

A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user’s behaviour, which means a user’s interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn’t consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

A Method of Interest Degree Mining Based on Behavior Data Analysis

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.

An Improved Collaborative Filtering Recommendation Algorithm Based on User Forgetting Curve

Collaborative filtering recommendation is one of the earliest and successful algorithms in the recommender system. The traditional collaborative filtering algorithm only relies on the user-item scoring matrix for recommendation, and fails to take into account the impact of user interest changes on recommendation weights. An improved similarity based collaborative filtering recommendation algorithm with user interest change is proposed in this paper. The proposed algorithm employed an improved item similarity calculation model and the Ebbinghaus forgetting curve to mimic the user interest tracking. A series of experiments were completed on the Netflix dataset and MovieTweeings dataset, compared with several classic traditional algorithms about the accuracy and coverage rate. The experimental results show that the accuracy of the proposed algorithm overwhelms the traditional algorithm with the improvement of 2%-5%.

Sparse online collaborative filtering with dynamic regularization

Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches.

THE METHOD OF CONSTRUCTING RECOMMENDATIONS ONLINE ON THE TEMPORAL DYNAMICS OF USER INTERESTS USING MULTILAYER GRAPH

The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time.

A Temporal Recommendation Mechanism Based on Signed Network of User Interest Changes

In recommender systems, one of critical tasks is to help users find their interested items among huge amount of items. The development of recommendation methods and techniques has driven many real-world recommendation applications. Desirable recommendations are vital both to the target users and the recommended items. Mining meaningful interest information from different time spans is crucial for recommendation precision. In this paper, the relations between different time spans are adopted to construct the signed networks and imitate users interest changes. First, we initially utilize temporal score information to divide different time spans. Then, the signed networks are formed at different time steps referring to users interest changes. Furthermore, considering the characteristics of signed networks, we define a new similarity measurement to grasp the common interest between two adjacent signed networks. A new adjacent matrix is obtained by weighting the network adjacent matrices of different time steps. According to the constructed dynamic evolutionary clustering model in a signed network, the nodes are divided into different clusters. Finally, the predicted ratings are calculated in each subclass instead of the entire system, which reduces the computational cost greatly. The extensive simulations are conducted based on two real-world datasets, Movielens and CiaoDVD, for demonstrating that the recommend accuracy is significantly improved using our scheme.

An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests

A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods.

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

Designing an e-commerce recommender system based on collaborative filtering using a data mining approach

E-commerce recommender systems have been converted to a very important decision-making helper for customers, and provide online personalised recommendations using information technology and customers' information. In the meantime, collaborative filtering (CF) recommender systems are one of the key components of successful e-commerce systems. Despite the popularity and successes of CF, these systems still face a series of serious limitations, including cold start, sparsity of user-item matrix, scalability and change of user interest during the time, that impede exact recommendations to customers. Although much research has been presented to overcome these limitations, no comprehensive model is yet offered to reduce them: 1) customer segmentation based on LRFM variables in the level of product category to evaluate the length of customer relationship with the company, recency, frequency, and monetary of purchasing product categories; 2) extracting association rules based on user-category matrixes in the level of each cluster; 3) customer segmentation according to demographic variables; 4) change of user-item matrix and reduction of its dimensions; 5) developing a new similarity function by weighted combination of results of segmentation methods and CF. According to the gained results, the proposed system of this research has resulted in the removal of traditional CF constraints and presents more accurate and appropriate recommendations for the preferences of customers.

Designing an e-commerce recommender system based on collaborative filtering using a data mining approach

E-commerce recommender systems have been converted to a very important decision-making helper for customers, and provide online personalised recommendations using information technology and customers' information. In the meantime, collaborative filtering (CF) recommender systems are one of the key components of successful e-commerce systems. Despite the popularity and successes of CF, these systems still face a series of serious limitations, including cold start, sparsity of user-item matrix, scalability and change of user interest during the time, that impede exact recommendations to customers. Although much research has been presented to overcome these limitations, no comprehensive model is yet offered to reduce them: 1) customer segmentation based on LRFM variables in the level of product category to evaluate the length of customer relationship with the company, recency, frequency, and monetary of purchasing product categories; 2) extracting association rules based on user-category matrixes in the level of each cluster; 3) customer segmentation according to demographic variables; 4) change of user-item matrix and reduction of its dimensions; 5) developing a new similarity function by weighted combination of results of segmentation methods and CF. According to the gained results, the proposed system of this research has resulted in the removal of traditional CF constraints and presents more accurate and appropriate recommendations for the preferences of customers.

Scalable Distributed kNN Processing on Clustered Data Streams

Recommender systems provide an important tool for users to find interested items from the massive amount of user-generated contents. As user interests often change over time and contents become available in a streaming fashion, it is highly desirable to support real-time recommendation that can adapt to changes in user interests and contents. If we represent both user interests and items by high-dimensional points in the same vector space, we can recommend to the user the $k$ items that are the nearest neighbors (kNN) of the user. The problem of real-time recommendation, thus, translates to computing the kNNs based on the most recent items when the user interests change. As such, the main issue we tackle in this paper is to efficiently process high-dimensional kNN queries over a sliding window on data streams. In particular, we are interested in developing a scalable distributed solution to be able to handle the ever-increasing number of users and volume of data. We propose a new index structure called the dynamic bounded rings index (DBRI) to index the data points in data streams. The basic idea is to first find a set of pivots and assign all points to their nearest pivot to form subsets and then partition each subset into finer-grained bounded rings that can be dynamically adjusted as points change. The design of DBRI lends itself to easy adoption in a distributed setting. We further present the distributed high-dimensional kNN query algorithm (DHDKNN) based on DBRI, aiming at reducing both the communication and the computational cost of query processing. The experiments demonstrate that our algorithm scales well and significantly outperforms the existing methods.

A Personalized Recommendation Method Based on Collaborative Filtering Algorithm

Collaborative filtering algorithm is a widely used recommendation algorithm. However, when applied to e-commerce personalized recommendation, it faces the following issues: firstly, how to consider the user's interest changes over time when getting similarity between the users more precise; secondly, how to use social networks to more accurately getting the nearest neighbor of users; and thirdly, how to consider the behavior of users who have the same interests and different ratings in making the predicted rating score of item more accurately; fourthly, how to use the inherent relation between product categories, such as internal relations, while recommending. In order to solve these problems, this paper improves the traditional collaborative filtering algorithm by integrating timing updates, trust relationship, optimization of predicted rating score and structured ideas. To distinguish users' past interest characteristics and their recent ones, by introducing the idea of timing update, this paper regards the user's shopping experience as a set of time periods, considering the influences of the users' interest at different time on the similarity of the users, and the influence of trust relationship between target user and similar users on the establishment of nearest neighbor set. On this basis, faced with the difference of evaluation criteria of different users on the same recommendation item, this study optimizes scoring method of similar users and gets a pre-scoring-based predicted rating score method for target user to recommend item. Furtherly, considering the relationship between the recommended item and other items, this paper also proposes an idea of relative recommending based on recommended item as a secondary recommendation. At the end of this paper, the proposed method is verified on the review dataset in MovieLens which is provided by the College of computer science and engineering of University of Minnesota. The experimental results show that the proposed method has obvious recommendation accuracy compared with the traditional collaborative filtering algorithm.

Recommendation Based on Users’ Long‐Term and Short‐Term Interests with Attention

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi‐GRU neural network with attention to model user’s long‐term historical preferences and short‐term consumption motivations. First, a Bi‐GRU network is established to solve the long‐term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short‐term interaction trajectory based on self‐attention is modeled to distinguish the importance of each potential feature. Finally, combined with long‐term and short‐term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state‐of‐the‐art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN‐based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long‐term and short‐term interest of users, respectively.

Research on Recommendation Algorithm Based on Ranking Learning

After analyzing the logistic regression and support vector machine's limitation, the author has chosen the learning to rank method to solve the problem of news recommendations. The article proposes two news recommendation methods which were based on Bayesian optimization criterion and RankSVM. In addition, the article also proposes two methods to solve the dynamic change of user interest and recommendation novelty and diversity. The experimental results show that the two methods can get ideal results, and the overall performance of the method based on Bayesian optimization criterion is better than that based on RankSVM.

An improved hybrid collaborative filtering algorithm based on tags and time factor

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems (RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor (TT-HybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes overtime. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy.

Movie recommendation based on bridging movie feature and user interest

The traditional collaborative filtering algorithms have bad performance in the case of data sparsity, and are difficult to track the change of user interest. Even though many improved algorithms are proposed to solve these problems, it is still necessary for further improvement. In this paper, a novel hybrid recommendation algorithm is proposed to resolve the two issues by bridging the movie feature and user interest. In the proposed algorithm, the movie feature vector is formed based on the attributes of the movie, and is combined with the user rating matrix to generate the user interest vector. The movie feature vector and user interest vector are mutually updated in an iterative way, and then the user similarity matrix is constructed based on the user interest vector, which is usually difficult to be obtained in the case of data sparsity. Furthermore, the long-term and short-term interests are considered in the generation of the user interest vector, which aims to make the recommendation results adapt to the change of user interest. The experiments on the Movielens dataset show that the proposed algorithm outperforms some existing recommendation algorithms on recommendation accuracy.

A framework for personalized adaptive user interest prediction based on topic model and forgetting mechanism

User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user’s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.