A Mobile Service Recommendation System Using Multi-Criteria Ratings

Multi-Criteria Recommender Systems (MCRS) have emerged as a significant advancement over traditional singlecriterion recommendation systems by incorporating multiple dimensions of user preferences. This paper presents a comprehensive comparative analysis of three prominent MCRS approaches: Weighted Sum, TOPSIS, and Fuzzy Logic-Based methods. Using the MovieLens 1M dataset extended with multi-criteria ratings (story, acting, direction), we evaluate these approaches across multiple performance metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, Recall, and F1-Score. Our experimental results demonstrate that while the Fuzzy Logic-Based approach achieves superior performance in handling uncertainty (MAE of 0.65, F1-Score of 0.80), the TOPSIS method provides the best balance between accuracy and computational efficiency. The Weighted Sum method, despite its simplicity, shows limitations in recommendation quality. The study concludes with recommendations for selecting MCRS methods based on specific application requirements and proposes future research directions focusing on hybrid models and deep learning integration.

The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.

In order to overcome the problems of poor operation function and low recommendation accuracy of the traditional high-quality curriculum resource recommendation system, this paper proposes and designs a new high-quality Curriculum Resource Recommendation System for educational technology specialty. With the support of the system framework, the system hardware module is designed, mainly including user registration and login module and high-quality course resource recommendation module, so as to realize the recommendation function of the system. The system software part constructs the user’s high-quality curriculum resources mining classification tree according to the user’s preference attribute value of high-quality curriculum resources, and obtains the recommended basic data. On this basis, the user’s preference for course resources is calculated, so as to complete the recommendation of high-quality course resources. The experimental results show that compared with the traditional recommendation system, the functions of the designed system have passed the test and have high recommendation accuracy.

Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. The main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. Three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble.

Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.

There have been significant advances made in the research of recommender systems over the past decades and have been implemented in both industry and academia. Recently, multi-criteria ratings are being incorporated into traditional recommender systems to further improve their quality, especially to handle the data sparsity and cold start issues. However, incorporation of multi-criteria ratings have improved the performance of the recommendation, but at the same time, multi-dimensionality issue also arises. This paper presents a clustering-based recommendation approach which is used for dealing with the multi-dimensionality issue in multi-criteria recommender systems. Here, we cluster the users based on their individual criteria ratings using K-means clustering and the intra-cluster similarity is computed using Mahalanobis distance measure for neighbourhood set generation. This improves the recommendations quality and predictive accuracy of both traditional and clusteringbased collaborative recommendations. The Yahoo! Movies dataset was used for testing the approach and the experiment conducted shows promising results.

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.

Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users' explicit and implicit preferences (most often with their permission). Frequently they provide relevant recommendations that increase the revenue or user engagement of the online services that operate them. However, when we evaluate the current generation of recommender systems from the point of view of the “recommendee,” we find that most recommender systems serve the goals of the business instead of their users' interests. Thus we believe that the big promise of recommender systems has yet to be fulfilled. We foresee a third wave of recommender systems that act directly on behalf of their users across a range of domains instead of acting as a sales assistant. We also predict that such new recommender systems will better deal with information overload, take advantage of contextual clues from mobile devices, and utilize the vast information and computation stores available through cloud‐computing services to maximize users' long‐term goals.

Large amount of information generated on the web is useful for extracting useful patterns about customers and their purchases. Recommender system provides framework to utilize this information to make suggestions to user according to their previous preferences. They are intelligent systems having decision making capabilities. This in turn enhances business profit. Recommender system endure from problems like cold start, fake profile generation and data sparsity. Inclusion of trust in recommender system helps to alleviate these problems to a great extent. The phenomenon of trust is derived from daily life exp eriences like believing the views/reviews suggested by friends and relatives for buying new things. The desideratum of this research pap er is to procure a survey on how trust can be incorporated in recommender systems and the advantages trust aware recommender sy stems have over traditional recommender systems. It highlights the techniques that have been used to develop trust aware recommenders and pros and cones of these techniques.

Learning instrumental variable representation for debiasing in recommender systems.

Recommender systems are powerful systems that give added value to business and corporation. They are relatively recent technology and they will only keep improving in the future. The most widely used algorithms for recommender systems are categorized into the traditional recommender and deep-based recommender system. The traditional recommendation algorithm suffers from sparse data that significantly degrades recommendation accuracy. The hybrid approaches are attempts to tackle recommendation challenges. This paper addresses the integration of deep learning into traditional recommendation approaches especially, Collaborative Filtering (CF) algorithms to get a significant accurate prediction. It proposes a hybrid deep CF recommender model called ConvSVD++ that tightly integrates Convolution Neural Network (CNN) and Singular Value Decomposition (SVD++). The proposed model incorporates items’ content, implicit user’s feedback along with explicit item-user interaction to enhance prediction accuracy and handle sparsity problem. The proposed model is evaluated and all baseline models based on Movielens- 1M datasets. The results are evaluated using Root Mean Squared Error (RMSE) metric and it is concluded that the proposed model ConvSVD++ outperforms the baselines models. Accordingly, it is concluded that integrating CNN with SVD++ algorithm improves rating prediction accuracy.

A novel topic-based framework for recommending long tail products

In helping internet users to locate useful information by recommending possible interesting information, Recommender Systems play an important role. Due to the perceived significance of social interactions in proposed systems, in recent years social guidelines have gained increasing popularity. There has been a substantial increase in the number of network sciences and a huge increase in the number of social networking sites, which can have a direct effect on different aspects of everyday life. Since the advent of the World Wide Web, Recommender Systems have been a further fast-growing market, which also has a proven financial significance because a well-focused online proposal often results in actual purchases. While at the outset, both the websites and the Recommender Networks had very different routes, as well as the groups of researchers working with it, in a variety of real-world networks, almost immediate synergies, have recently started. This is just the beginning, however: there is still a range of potentially valuable synergies to explore. This chapter describes the concept of the recommended systems in the social networking context and addresses both traditional Recommendation Systems and the new Recommendation Systems, mostly through collaborative filtering and content-based approaches. They also describe a thorough analysis of the need to implement the social networking structure, the problems and challenges of the Recommender Systems, and the creation of capacity-building systems for social recommendations.

Social knowledge-based recommender system. Application to the movies domain