User Similarity Calculation Research Articles

UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement.

In today's society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user's behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users' questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users' interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.

UPTDNet: A User Preference Transfer and Drift Network for Cross-City Next POI Recommendation

Cross-city point of interest (POI) recommendation for tourists in an unfamiliar city has high application value but is challenging due to the data sparsity. Most existing models attempt to alleviate the sparsity problem by learning the user preference transfer and drift. However, they either fail to simultaneously model the preference transfer and drift in both long- and short-term user preferences or cannot accomplish the task of the next POI recommendation, which is crucial for a wide spectrum of applications ranging from transportation and urban planning to advertising. To address the limitation, we proposed a user preference transfer and drift network (UPTDNet) for cross-city next POI recommendation. UPTDNet excels at cross-city recommendations by learning the transfer and drift of both long- and short-term preferences. For short-term preference, dual recurrent neural network-based (RNN-based) branches are designed to model preference transfer from tourist’s current city and drift among different user roles. For long-term preference, a mapping function and user similarity calculation are employed for preference transfer from the tourist’s home city and drift among individual users. Experiments are conducted on the Gowalla and Foursquare datasets, and the results show that UPTDNet consistently and significantly outperforms state-of-the-art models by an average of 10.22% to 22.63% in the next POI recommendation task. Ablation study and further analysis validate the effectiveness and plausibility of considering both user preference transfer and drift in the cross-city recommendation.

Design of Personalized News Recommendation System Based on an Improved User Collaborative Filtering Algorithm

To solve the problem of information overload in the field of news, this paper designs and implements a feasible news recommendation system, where the front-end web page is made by Django framework, whose performance is optimized by bootstrap and jquery, while in the back-end design, the original user similarity calculation method is improved by adding the time attenuation factor, and a news recommendation model based on user collaborative filtering (CF) algorithm is proposed. Experimental results show that the proposed algorithm achieves highest recall, accuracy, and F1 score ratio compared with other algorithms, which indicate that the proposed algorithm has better performance.

Exploring indirect entity relations for knowledge graph enhanced recommender system

Knowledge graph (KG)-based recommendation models generally explore auxiliary information to alleviate the sparsity and cold-start problems in recommender systems. Previous approaches enhance representations of users and items by exploring the influence of multi-hop neighbors. However, existing works fail to consider the indirect feedback for improving user representation and the diversity of the multi-hop neighbors for enriching item representation. To this end, we present a novel recommender system, called Entity Relation Similarity and Indirect Feedback-based Knowledge graph enhanced Recommendation (ERSIF-KR) to enhance representation learning in KG-based recommender systems. In addition, our model exploits indirect feedback of items that are not directly interacted with users to alleviate the exposure bias while enhancing user similarity computation when learning user representation. Moreover, our method directly incorporates representation of multi-hop neighbors into the target item embedding with weights determined by the correlations between high-order and low-order relations, which can significantly boost the item representation learning. Extensive experiments on three real-world datasets demonstrate that our model achieves remarkable gains in terms of recommendation performance and model convergence time, and effectively alleviates the sparsity and cold start problems.

Analysis of Precision Service of Agricultural Product e-Commerce Based on Multimodal Collaborative Filtering Algorithm

The times are advancing, the Internet is constantly advancing, and there are many examples of the combination of technology and life. The way people buy agricultural products is no longer limited to the physical market, but is being transformed into an agricultural product e-commerce website to achieve digital transformation. So far, a large number of agricultural products have been collected on the agricultural product e-commerce website, they come from all over the country, and different types and different regional agricultural products have appeared. On the other hand, users are faced with a large number of commodities on the Internet. If they simply use search engines, consumers need to spend a lot of time to find the commodities they are interested in by browsing. Searching from the recommended ones greatly reduces the product set and optimizes the performance of the e-commerce platform. Therefore, the generation of the personalized recommendation system meets the needs of the market. This research will improve the related recommendation algorithm by improving the user similarity calculation method based on artificial intelligence technology, improve the execution efficiency and accuracy of the improved algorithm, and use related technologies to develop an agricultural product e-commerce recommendation system. It has important theoretical and practical significance to save users’ browsing time, improve users’ shopping experience, and speed up the construction of agricultural product e-commerce.

TT-graph: A new model for building social network graphs from texts with time series

Social network analysis is a fundamental problem inherent in various applications, which can be handled mainly based on a graph model given in advance. However, it is generally ignored in most existing studies that the social networks may only contain users and no users' connections are explicitly available. Then the connections between users play a considerable role in building the graph, and it is necessary to calculate users' similarities. Traditional methods of user similarity calculation are extensively based on the topics of text content because they can effectively reflect users' interests. Nevertheless, these methods mostly ignore the importance of time series in the texts, where the texts with time series can reveal the activity trends of users in social networks. In this work, we explore a new problem of building a social network graph over text with time series. Our basic idea is that social media users are more similar if they have similar text semantics in similar time sequences. To obtain the semantics of text with time series, we extract topic words of each user from the corresponding text with our proposed Time-Biterm Topic Model (T-BTM), which improves the BTM model by taking the time-topic distribution into account. On this basis, we further propose a novel time series-based graph model with text, called Text with Time series for Graph (TT-Graph) model, which explicitly considers the user similarity and time series similarity. With the TT-Graph model, we propose novel methods for topic detection, community detection, and link prediction in social network analysis. Extensive experiments demonstrate that topic detection, community detection and link prediction can be effectively conducted on the TT-Graph model, and the credibility of our model can be proved.

E-commerce New Media Marketing Based on Data Technology

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the accelerating pace of life of Chinese residents, the expansion of the new media marketing market and the surge in the number of online shoppers have prompted e-commerce platforms to generate a wealth of e-commerce sales data. Faced with complex sales data, merchants need to analyze and understand the data in depth. Based on this, the purpose of this article is to study the application of e-commerce new media marketing based on data mining technology. This article first summarizes the basic theories of data mining, and then studies and analyzes its mining process, functions and methods. This paper systematically expounds the data preprocessing process, data preprocessing method selection and system algorithm realization of data mining technology in e-commerce new media marketing, and uses comparative method, observation method and other research forms to study the subject of this article. Experimental research shows that when user similarity calculation adopts the method Attribute-SimRank in this paper, the average hit rate of product recommendation for cluster center users is in most cases higher than that of SimRank and Attribute methods.</span>

Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.

Communication-Based Book Recommendation in Computational Social Systems

This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users’ check-in behaviours. Individual’s check-in behaviour can be under the influence of location region and time slots, so user’s feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.

Personalized Retrieval in the Medical Domain: A NoSQL Solution Based on Ontology Building

Managing medical information in a Big Data context is a challenging task since searching for relevant information in a large volume of data needs advanced treatments. Medical data is a special type of data because it comes from different sources and in different formats and encapsulates medical knowledge. Personalized retrieval is necessary when it comes to medical data management. In fact, the patient’s medical record needs to be taken into account in order to offer relevant documents since it contains his/her medical history. The proposed approach offers an ontology building process based on the patient’s medical record. The built ontology is then used for personalized information retrieval as well as user similarity computation. The approach is composed of three layers: (1) Data layer, (2) Treatment layer and (3) Semantic layer and offers three treatments: (1) Ontology building, (2) Query reformulation and (3) User similarity computation. An application supporting all three layers has been implemented and it allowed an experimental evaluation of the proposal. The results show an improvement in the relevancy of returned medical documents.

Research on power-law distribution of long-tail data and its application to tourism recommendation

PurposeOne challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.Design/methodology/approachUsing Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.FindingsThe comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.Originality/valueTRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.

Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks

One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems’ algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.

Multiple collaborative filtering recommendation algorithms for electronic commerce information

ABSTRACT The information filtering recommendation algorithm has poor security, low efficiency and user satisfaction, and high network energy consumption. A multiple collaborative filtering recommendation algorithm for e-commerce information based on user preferences is proposed. In the time series data of the original e-commerce information, using the sliding window for feature extraction, the trend value characteristics of the information variable will be obtained, and the suspected outfielders will be determined based on the trend feature value. Using the resulting eigenvalues for secondary detection, the abnormal data points are further judged, and the abnormal data therein is filtered using a filter. The filtering results are substituted into the information filtering recommendation based on the user’s preferences. According to the user’s preference knowledge, the user’s preference space matrix is constructed to complete the user similarity calculation, and the nearest neighbors with the same or similar preferences are obtained. The demand of the target user is predicted using the nearest neighbor and preference knowledge, and the multiple collaborative filtering of the electronic commerce information is completed. Experiments show that the user satisfaction of this algorithm is in the range of 91%–99%, with high recommendation efficiency and security, low network energy consumption, and strong practicality.

A novel next new point-of-interest recommendation system based on simulated user travel decision-making process

Abstract POI(point of interest) recommendation systems have been widely investigated in recent years. Currently, most POI recommendation systems only recommend POIs that may be visited by users in the future, and rarely consider next new POI recommendation based on the current time and the current location of a particular user. In fact, next new POI recommendation problem is more challenging for the reason that multiple factors associated with both POIs and users need to be comprehensively incorporated in a unified recommendation system. In this paper, we design a novel and effective next new POI recommendation system. Our system simulates a user’s travel decision-making process by weighing two important factors that affect a user’s travel decision: preference factors and geographic factors. First, we use tensor to model user’s check-in history and dynamically predict user preferences. Then, in order to characterize the influence of geographic factor on individual users, we designed a personalized user similarity calculation method and fitted curves for the target user to reflect the relationship between travel distance and travel probability. Finally, a recommendation list is generated by combining the effects of these two factors on a particular user. Compared with the state-of-the-art POI recommendation approach, the experimental results demonstrate that our system achieves much better performance.

A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems

Recommender systems are intelligent programs to suggest relevant contents to users according to their interests which are widely expressed as numerical ratings. Collaborative filtering is an important type of recommender systems which has established itself as the principal means of recommending items. However, collaborative filtering suffers from two important problems including cold start and data sparsity. These problems make it difficult to accurately compute user similarity and hence to find reliable similar users. To deal with these problems, a novel recommender method is proposed in this paper which is based on three different views of reliability measures. For the first view, a user-based reliability measure is proposed to evaluate the performance of users’ rating profiles in predicting unseen items. Then, a novel mechanism is proposed to enhance the rating profiles with low quality by adding a number of reliable ratings. To this end, an item-based reliability measure is proposed as the second view of the reliability measures and then a number of items with highest reliability values are selected to add into the target rating profile. Then, similarity values between users and also initial ratings of unseen items are calculated using the enhanced users’ rating profiles. Finally, a rating-based reliability measure is used as the third view of the reliability measures to evaluate the initial predicted ratings and a novel mechanism is proposed to recalculate unreliable predicted ratings. Experimental results using four well-known datasets indicate that the proposed method significantly outperforms other recommender 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.

Improved user similarity computation for finding friends in your location

Recommender systems are most often used to predict possible ratings that a user would assign to items, in order to find and propose items of possible interest to each user. In our work, we are interested in a system that will analyze user preferences in order to find and connect people with common interests that happen to be in the same geographical area, i.e., a “friend” recommendation system. We present and propose an algorithm, Egosimilar+, which is shown to achieve superior performance against a number of well-known similarity computation methods from the literature. The algorithm adapts ideas and techniques from the recommender systems literature and the skyline queries literature and combines them with our own ideas on the importance and utilization of item popularity.

The Improvement of Personalized Recommendation System Based on Sparse Data of Financial Products

In the era of big data, innovative marketing of internet finance confronts with new challenges. How to provide users with precise customized financial service, has become a problem of further expansion of internet financial market. Based on this background, the research integrates the ideology of concept hierarchy into the traditional personalized recommendation algorithm, and proposes an improved trick, which is punishing user similarity computation of popular items. And the research integrates the ideology of community mining into recommendation algorithm, making EO algorithm as the basis of community division algorithm, which combines improved index of user similarity and the Q of community discovery to build a new model of community discovery and divide the structure of social network. Optimizing the procedure of recommendation system solves the problems of data sparsity, cold start and system scalability. In the end, selecting the data from the Mint, which is a financial institution in America, demonstrates the improved effect that the two innovative schemes proposed in this research contribute to the accuracy of the recommendation system and the precision of community discovery.