Top Research Papers on Recommender Systems

A comparison was made between precision and an improved precision algorithm, and the result of improved precision is slightly higher than precision in different cutoff values and different dimensions of eigenvalues.

This work deals with the understanding of the underlying models for recommender systems and describes their historical perspective, and analyzes their development in the content offerings and their impact on user behavior.

In this section a model for recommendation systems, based on a utility matrix of preferences is introduced, and the concept of a " long-tail, clustering-based recommendation system", which can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering.

In this section a model for recommendation systems, based on a utility matrix of preferences is introduced, and the concept of a " long-tail, clustering-based recommendation system", which can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering.

The main problem solved by collaborative filtering methods/recommender systems can be phrased in a number of ways: user-based recommendations, content-based recommendation systems, and collaborative recommendation systems.

In this section a model for recommendation systems, based on a utility matrix of preferences is introduced, and the concept of a " long-tail, clustering-based recommendation system", which can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering.

This thesis focuses on the improvement of recommendations within personalization processes applied to support users overcome the information overload problem and proposes four group recommendation models to overcome each of the following limitations of previous techniques.

The invention is used for recommendation based on all history information under the condition that the computation cost is not increased obviously, and the recommendation results are complete.

Recommender systems are becoming a large and important market, with commerce moving to the internet and the ability to keep a larger stock of products, one of the biggest hurdles is to organize and manage the supply chain.

A unifying framework is introduced that positions the research work, that has been done so far in a scattered manner, in a three stage model, in an attempt to improve the quality of recommender systems.

A recommending system that recommends content so as to keep constant network traffic between a user terminal and a server, and a recommending server that transmits recommendation data, which are connected via a network.

A hybrid recommender system for the movies ranking, where a movie based recommender system suggests the user about the movie that he should rank after performing the intelligent analysis using weighted approach.

The novelty of the proposed approach is that it provides personalized recommen- dations regardless of the research field and regardless of the user’s expertise.

This paper aims to develop à recommandation System using based-content and collaborative filtering in order to recommend potential profiles for a new job offer using based-content and collaborative filtering.

In this review, the approaches, techniques and application of recommendation system are listed out which helps to map future direction of world wide web.

Recommender systems give advice about products, information or services users might be interested in to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services.

This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.

This study searched for the survey paper on recommendation system, a method of making automatic predictions about the interests of customers by collecting information from number of other customers, for that purpose many collaborative base algorithms are used.

Recommender systems are web-based applications that aim at helping customers in the decision making and product selection process (Resnick & Varian, 1997). The most prominent example is the online bookstore amazon.com, where collaborative filtering techniques are used to exploit similarities in the user profile which is based on the navigation and buying history: The main idea is to identify users who presumably have similar preferences and recommend those items which were bought by other users with a similar interest profile. Another technical approach is content-based filtering which builds ...

A hybrid approach that uses content-based methods with collaborative filtering is discussed, which avoids the so-called new item or item cold start problem and also avoids the inability to recommend to new users whose profile is not known.

Recommender systems give advice about products, information or services users might be interested in. They are intelligent applications to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services. And they are probably among the most prominent applications having a substantial impact on the performance of e-commerce sites and the sector in general. In fact, even if the problem of supporting a choice decision process is quite old, it is only with the advent of the WWW that we had at disposal, in a lar...

This paper has proposed a performanceoriented security solution for a personalized wellness recommender system that results in another layer of complexity making the performance requirement hard to achieve.

The need for including news sources in news recommendation is explained and a news source recommendation method is proposed by finding out the implicit relations and similarities between news sources by using semantics and association.

This study investigates services with their unique characteristics that imply explicit considerations during implementation of service-oriented recommendation systems and proposes a generic recommendation model by considering service customer and system users requirements.

A mobile system for automatically recommending content, a content recommendation system, and a content recommending method are provided to supply information about content based on the moving speed of a user, a surrounding weather, or selection of people around a user thereby effectively recommending the content.

The embodiment of the invention saves the cost of marking tag content and improves the accuracy of tags content and tag intensity.

The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset, and observe itemCF contains the most accurate rate.

In this paper, an experiment is detail illustrating the scenario where a Personality-based Recommender System is applied, followed by the description of the approaches usually used in order to implement them.

An overview of the field of recommender systems is presented and various limitations of recommendation methods and their advantages are described.

This work describes a multi-stakeholder, multi-objective problem in the context of CommBank Rewards (CBR) and describes how a system that balances the objectives of the bank, its customers, and the many objectives from merchants into a single recommender system is developed.

An offline comparative evaluation of commonly used recommendation algorithms of collaborative filtering using the BookCrossing data set containing 1,149,780 user ratings on books shows the disparity of evaluation results between the RS frameworks.

A novel topic diversity metric is proposed which explores hierarchical domain knowledge, and the recommendation diversity of the two most classic collaborative filtering algorithm with movielens dataset is evaluated.

The recommendation method in the heterogeneous network uses a uniform model to simulate a plurality of objects with different types and a complex relation that may exist in the objects, and finishes recommendation perfectly by the model.

Multimedia information which approach a user preference can be recommended to a user at an appropriate recommending time, thereby greatly improving advertisement effect without disturbance to the user.

The architecture and implementation of the expertise recommendation system details the work necessary to fit expertise recommendation to a work setting, and begins to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.

The invention provides a recommendation searching method and recommendation searching system and by the method and the system, accuracy of searching is improved.

A number of requirements that a classification scheme must meet in order to be useful in the context of the WRS are identified and four existing knowledge classification schemes are evaluated with respect to these requirements.

A generic framework that delivers "contextual recommendations" that are based on the combination of previously gathered user feedback data, context data, and ontology-based content categorization schemes is proposed.

The technical scheme not only guarantees recommending qualities, but also provides high-speed real-time recommending service, and has the advantages of being high in expandability, and suitable for different application contexts.

The software specialists can be more objectively selected for the user according to software use situation of the user, so that the software recommendation method which is more accurate, more proper and more authorized can be provided for theuser.

A hybrid model was developed by combining all the individual models which improved the overall RMSE and was applied to recommend restaurants to users.

A collaborative recommender system that recommends online courses for students based on similarities of students' course history is presented and it is noticed that clustering students into similar groups based on their respective course selections play a vital role in generating association rules of high quality when compared with the association rules generated using the whole set of courses and students.

A recommendation system for university elective courses, which recommends the courses based on the similarity between the course templates of students is introduced, which shows that applying ALS in this domain is superior to collaborative based with 86 percent of accuracy.

The resource recommendation device can be applied to the automatic updating of a website, the problem of untimely information updating caused by insufficient manpower is avoided, the updating frequency and the updating efficiency of the page information are increased.

Abstract: Recommendation algorithms are widely used, however many consumers want more clarity on why specific goods are recommended to them. The absence of explainability jeopardizes user trust, satisfaction, and potentially privacy. Improving transparency is difficult and involves the need for flexible interfaces, privacy protection, scalability, and customisation. Explainable recommendations provide substantial advantages such as enhancing relevance assessment, bolstering user interactions, facilitating system monitoring, and fostering accountability. Typical methods include giving summaries...

Whether customers make purchase decisions online using the suggestions recommended across various product lines is examined to understand whether respondents of different demographic characteristics make purchase related decisions based on the suggestions by recommendation systems.

This paper investigates whether introducing a healthy bias in a recipe recommendation algorithm, and displaying a healthy tag on recipe cards would have an influence on people’s decision making, and builds three different recipes recommender systems that recommends recipes matching users’ preferences.

An overview of existing recommender approaches used in tourism is presented and their relevance taking into account tourism context and specificities are discussed.

This technical report presents the solution of RecSys Challenge 2022 focusing on the fashion recommendation using six effective retrieval strategies in retrieval step and ensemble five ranking models by taking average of their outputs.

This work introduces a Web-based visual tool for exploring and organising recommendations retrieved from multiple sources along dimensions relevant to cultural heritage and educational context and envision a system which derives user's interests from performed actions and uses this information to support the recommendation process.