Personalized Recommendation Service Research Articles

The Effect of Variety in Past Consumption on Openness to Personalized Recommendation Services

The Effect of Variety in Past Consumption on Openness to Personalized Recommendation Services

Movie Recommendation System using RNN Approach

The problem of information overload has been made worse by the quick advancement of information technology and the quick expansion of the Internet. In response to this issue, recommender systems have developed, assisting users in finding relevant material. The study of personalized recommendation services has seen a change in focus as a result of the complexity of the social setting. We present a novel recommendation approach based on social network recurrent neural network in order to address the sparsity problem of recommender systems while enhancing their accuracy and diversity in complicated scenarios. Using this approach, we group users and take a number of intricate criteria into account.

Recommendation Approach Based on TwinBERT Model

In order to provide more personalized recommendation services for users as well as improve the platform's traffic and user satisfaction, a recommendation method based on the TwinBERT model has been proposed. As a matter of fact, this method combines not only the basic information of users and items but also user comments or item text information to predict users' behaviors and make more accurate recommendations. The text information in the dataset is encoded in sentences using the BERT model, and the encoded text features are fused with other features to obtain a representation of user and item embedding vectors. These vectors are then used to predict the user's rating of the item and recommend relevant items for the user. The proposed method was tested on the Amazon review dataset and the MovieLens dataset, and the experimental results showed that it improved over traditional recommendation algorithms in terms of accuracy and efficiency.

Research on the application of personalized recommendation

With the development of the Internet, people have more opportunities to be exposed to personalized recommendations, and the heat of related topics has increased. The purpose of this paper is to summarize the application principle and use of personalized recommendations by analyzing the relevant data, so that people can have a better understanding of personalized recommendations and make preparations for future related research. This paper first introduces the general development context of the personalized recommendation system and its uniqueness. The analysis finds that personalized recommendations can provide users with the most relevant and valuable information, goods, or services according to their interests, preferences, and behaviors. Secondly, this paper explores the application scenarios of personalized recommendation systems in different fields, including but not limited to video content, e-commerce, and online learning. In addition, this paper also lists the application of YouTube and Netflix in video content recommendation, as well as personalized recommendation services in e-commerce and online learning platforms. In particular, the rise of intelligent adaptive online learning systems and their adaptability in the field of education and training are analyzed in detail. Through this research, it can be found that a personalized recommendation system is very beneficial to people's production and life, but at present, personalized recommendation is still in the development stage, and there are many problems to be solved.

Construction of personalized learning service system based on deep learning and knowledge graph

Abstract With the development of information technology, it is difficult for people to find effective information from the massive Internet information, and personalized services are in short supply. This paper explores the essential association between knowledge graphs and deep learning and examines the construction process of knowledge graphs. Based on the content and collaborative filtering recommendation method, the theoretical method of personalized learning service recommendation is being studied. Knowledge graphs are introduced in collaborative filtering to accurately portray user and item vector representations. The knowledge graph is used to represent the TransR model, forming an entity space and a multivariate relationship space. The students’ demand for personalized learning service systems is analyzed from the user’s point of view, the personalized learning service system is constructed, and the accuracy of the personalized learning service system constructed in this paper is analyzed. The accuracy rate of DLKG is 0.1425 when K=10, which is the highest among the five models. In the effect of personalized teaching, the mean value of the metacognitive ability of the students in the experimental class is higher than that of the control class by 0.4155, 0.0261, 0.3995, 0.7967, respectively, and the experiment proves that the personalized learning service system based on the DLKG model proposed in this paper is more effective.

Information Service Innovation in College Libraries Based on Big Data Analysis

Abstract This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image construction, which in turn gives a personalized wisdom service recommendation structure. Secondly, user long and short-term preference fusion is carried out by introducing a self-attention mechanism and GRU network, and then personalized retrieval recommendation based on user long and short-term preference is realized. Finally, an investigation is carried out to determine the effectiveness of the personalized wisdom information service model through empirical analysis. The results show that the mean value of literature and history in the user portrait category III is 1.553, the change in the borrowing of books in the I major category decreased by 31.94% from 2018 to 2022, and the average satisfaction of users for the personalized recommendation service is 83.52%. This shows that the personalized intelligent information service model of the college library can realize the innovation of library information service and further improve the satisfaction of users for the college libraries.

Civic education reform based on deep reinforcement learning model

Abstract The integration of artificial intelligence technology into education is an inevitable trend of scientific progress and educational reform, and how to use artificial intelligence technology and ideological and political education reform is called a key research direction in the education sector. Aiming at the problems of cold start in personalized recommendation system, lack of interpretability of recommendation results, and ignoring the implicit features of the course for better acceptance of recommendation results by learners, the BPRMF model based on deep learning is proposed to be applied to the problem of recommendation of Civics and Political Science course, which not only models learners’ preferences and combines with course attribute features to generate recommendation rating ranking list and provide personalized recommendation service. Then the study of Civics education reform is conducted, mainly analyzing the change in teaching methods based on big data, machine learning, and deep learning technologies to promote secondary school students. The performance of the BPRMF model is evaluated in comparison with the BPRMF model under different k values. It is concluded that the accuracy rate of the BPRMF model is 8.9%~12.01% higher than UBCF and 8.07%~10.26% higher than IBCF, but with the increase of k value, the recall rate will gradually pull away from other models and optimize the recommendation system to some extent. This study is beneficial to ideological education in the implementation process to better utilize the opportunities, meet the challenges, and develop efficiently.

Two-stage travel itinerary recommendation optimization model considering stochastic traffic time

In an increasingly competitive market, most online tour operators have launched personalized travel itinerary recommendation services. Different from previous studies, this research considers the travel itinerary planning problem under total time limit and uncertain traffic time. This problem requires two stages of decision-making: firstly, selecting the tourist attractions to visit from the candidate attractions based on maximizing the popularity utility of tourists; secondly, planning the tourist attraction visit sequence under stochastic traffic time to maximize the tourist activity utility. Therefore, a two-stage stochastic optimization model with chance constraint is constructed and then solved by the sample average approximation (SAA) method. In order to verify the effectiveness of our model, we introduced two benchmark models for comparative analysis. The results show that in the worst case, our model increases the reliability level of travel itineraries by almost 40% compared with the two benchmark models. In addition, case studies of two large cities, i.e., Nanjing and Beijing, indicate that a tour operator can optimize travel itinerary recommendations by improving tourists utility as well as their own profitability without much loss of reliability.

Research on Weibo New Word Recognition based on Weibo Data and Statistical Information

One of the key challenges in the field of Chinese information processing is the recognition of Weibo new words, which has a profound impact on machine translation and text classification. As Weibo has become the most used social platform for internet users, mining new vocabulary from Weibo data not only helps to deeply understand the data itself, but also provides personalized recommendation services for users. Although a large amount of research has focused on the recognition of Weibo new words, specialized research in this field is still scarce. In this article, we propose a Weibo new word recognition strategy that combines Weibo content features and statistical information. Firstly, extract repetitive vocabulary from Weibo topic names, and then use various methods such as absolute frequency, relative frequency, mutual information, and information entropy to filter for incorrect vocabulary. The experimental results show that by setting appropriate thresholds, incorrect vocabulary can be effectively filtered out, thereby improving recognition performance.

MGMASR: Multi-Graph and Multi-Aspect Neural Network for Service Recommendation in Internet of Services

With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.

Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes. In this study, we propose a novel RS that leverages ensemble learning by integrating sentiment analysis of textual data with collaborative filtering techniques to provide users with more precise and individualized recommendations. Our system was developed in three main steps. Firstly, we used unsupervised “GloVe” vectorization for better classification performance and built a sentiment model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Secondly, we developed a recommendation model based on collaborative filtering techniques. Lastly, we integrated our sentiment analysis model into the RS. Our proposed model of SA achieved an accuracy score of 93%, which is superior to other models. The results of our study indicate that our approach enhances the accuracy of the recommendation system. Overall, our proposed system offers customers a more reliable and personalized recommendation service in e-commerce.

A spatial–temporal hypergraph based method for service recommendation in the Mobile Internet of Things-enabled service platform

With the rapid progress of Mobile Internet of Things (MIoT), Location-Based Services (LBSs) have become widely popular due to the deployment of diverse devices and facilities. The utilization of spatial–temporal information has led to a considerable improvement in service recommendation in the MIoT-enabled service platform. In practical scenarios, the interaction between the user and the service is often initiated by the joint effect of previous service invocations. Many-to-many and high-order spatial–temporal relations exist among services, which are inadequately reflected by prevailing methods that rely on ordinary graphs and consider spatial–temporal relations among services as pairwise connections. Additionally, most methods indiscriminately conflate the separate influences of spatial and temporal information on user preferences, which can lead to suboptimal service recommendation performance. To this end, we propose an approach powered by spatial–temporal hypergraphs (STH) for personalized service recommendation in the MIoT-enabled service platform, which is inspired by the deep learning paradigm. Specifically, we first construct spatial and temporal hypergraphs based on spatial–temporal information. Then, we develop the spatial–temporal hypergraph neural network-based encoders to learn spatial and temporal representations of services. Finally, we introduce an auxiliary task to differentiate between distinct spatial–temporal effects for improved service recommendations. We conduct extensive experiments on four real-world datasets, and the results demonstrate the effectiveness of STH. Thus, STH not only enhances the user experience but also improves service management in the MIoT-enabled service platform.

Ancillary Services in Targeted Advertising: From Prediction to Prescription

Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the net present value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy—that is, scalable and efficient—to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%–3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift.History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0491 .

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.

Preference-aware Bayesian Personalized Ranking for Point-of-interest recommendation

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods.

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

An innovation service system and personalized recommendation for customer-product interaction life cycle in smart product service system

Under the background of the continuous upgrading of customers' personalized needs, enterprises will carry out a series of transformation and upgrading into mass personalization model to adapt to the flexible and changeable market environment, and meet customers' needs. Moreover, the service for customers based on Customer-product Interaction Life Cycle (CILC) is a new innovation service, which is gradually evolved from the pursuit of general functional requirements of products to the smart product service system (SPSS) based on customer participation in product manufacturing and customer-product interaction. Therefore, this paper studies the innovation service system for CILC in SPSS under the new model of mass personalization. Firstly, a comprehension service system for CILC in SPSS is proposed to provide the top-level framework and decision-making of service for enterprises. Secondly, a personalized service recommendation for CILC in SPSS based on improved collaborative filtering algorithm is proposed to realize the recommendation, expansion and value-added of service resources for enterprises. Thirdly, a calculation example of personalized service recommendation for CILC in SPSS is giving. The service system for CILC in SPSS proposed in this paper is an extension and supplement to the theory of SPSS. At the same time, the research of this paper can give a reference for enterprise to plan, set, select, carry out, and maintain service items for CILC in SPSS.

A Momentum-Accelerated Hessian-Vector-Based Latent Factor Analysis Model

Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions data is a hot yet thorny issue. An efficient approach to it is latent factor analysis (LFA), which commonly depends on large-scale non-convex optimization. Hence, it is vital to implement an LFA model able to approximate second-order stationary points efficiently for enhancing its representation learning ability. However, existing second-order LFA models suffer from high computational cost, which significantly reduces its practicability. To address this issue, this paper presents a Momentum-accelerated Hessian-vector algorithm (MH) for precise and efficient LFA on HiDS data. Its main ideas are two-fold: a) adopting the principle of a Hessian-vector-product-based method to utilize the second-order information without manipulating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning scheme for accelerating its convergence rate to a stationary point. Experimental results on nine industrial datasets demonstrate that compared with state-of-the-art LFA models, an MH-based LFA model achieves gains in both accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with the algorithms, e.g., a second-order algorithm, which implicitly rely on gradients.

Differential Privacy-based Personalized Recommendation Service for "Helping Farmers" Tourism in Guizhou Province

The revitalization of the countryside is an important strategy proposed in the report of the 19th Party Congress. Rural tourism in Guizhou Province is the key to the development of "big tourism" in the 14th Five-Year Plan of the province, and also the driving force of rural revitalization. Most of the current rural tourism personalized recommendation systems rely on user information, and the exposure of sensitive user information has become an obstacle to the development of smart tourism. Through the genetic matrix decomposition recommendation algorithm based on satisfying differential privacy protection and differential privacy recommendation algorithm for sparse data, privacy protection of similarity calculation, accuracy of recommendation and data security are realized. Actively develop secure and personalized rural tourism recommendations so that the majority of farmers can benefit from them.