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Proceedings ArticleDOI

A Collaborative Filtering based Recommender System for Suggesting New Trends in Any Domain of Research

01 Feb 2019-pp 550-553
TL;DR: A research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user.
Abstract: Recommender system, an information filtering technology used in many items is presented in web sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media in general. In today’s world, time has more value and the researchers have no much time to spend on searching for the right articles according to their research domain. More than 250 research paper recommender systems were published and the quantity of research papers published every day is increasing rapidly. Thus it needs an efficient searching and filtering mechanism to choose the quality research papers, so that the effort and time of researchers can be saved. The recommender system proposed here uses three major factors used for building this system which includes datasets, prediction rating based on users and cosine similarity. The ratings are made by user which will be determined by the number of accurate ratings they provide. The results are then sorted by using cosine similarity. We propose a research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user.
Citations
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Proceedings ArticleDOI
PV Devika1, K Jyothisree1, PV Rahul1, S Arjun1, Jayasree Narayanan1 
06 Jul 2021
TL;DR: In this paper, a collaborative filtering method was used to select the right book for each reader based on their interests and also the data from several different readers, and the authors used a machine learning model to recommend books that are of interest to them.
Abstract: The main purpose of a recommendation system is that it will suggest items to users easily making their life easier. Today the quantity of facts with inside the net increase very hastily and those want few instruments to seek out and access appropriate data. One of such tools is named recommendation system. Recommendation systems propose products to the users which are most relevant to that particular user. Nowadays, online book marketing websites compete with one another in a variety of ways. One of the most powerful methods for increasing benefit and retaining customers is a recommendation framework, which can recommend books that are of interest to the customer. So the fundamental reason for this project is to support folks that have an interest in reading and to influence those individuals who are inculcating the habit of reading. By building a book recommendation system we tend to aim to assist people opt for the proper book that interests them and so encouraging them to read more. With the assistance of data sets and machine learning we believe we will choose the right book for someone supported their interests and also the data from several different readers. therefore here we use a collaborative filtering method.

10 citations

Journal ArticleDOI
12 Aug 2021
TL;DR: Friends Recommendation System identifies the behavior of users found in the dataset like user having number of followers, number of followings, common friends between followers and followings and provides the friend suggestions for the users they can follow.
Abstract: Friends Recommendation System identifies the behavior of users found in the dataset like user having number of followers, number of followings, common friends between followers and followings and provides the friend suggestions for the users they can follow. Recommendation system can also be used in other areas like recommending webpages to users in searching engines like Google, Explorer, Microsoft Edge. Recommending music in wynk, video recommendation in you tube, movie recommendation in amazon prime, recommendation of products to purchase in e-commerce applications like Flipkart, Amazon. Machine learning is used for providing recommendation on social networking application like Facebook, Instagram, Twitter Etc., In this Paper, recommendation of friends is done for Facebook. Similarity Coefficient calculations can be done using Jaccard Distance, Cosine Distance. Ranking Measures are done using Page Rank. Measuring the F1-Score and comparing the accuracy of different machine learning algorithms. These helps in finding which algorithm is more accurate in providing friends recommendations in social media recommendation system.

7 citations

Journal ArticleDOI
TL;DR: In this paper , a new Sentiment Scoring Model (SSM) based on Long/Short-Term Memory and a combination function was proposed to catch the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset.
Abstract: Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p < 0.05.

3 citations

Journal ArticleDOI
TL;DR: In this article, a new Sentiment Scoring Model (SSM) based on Long/Short-Term Memory and a combination function was proposed to catch the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset.
Abstract: Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p

3 citations

Proceedings ArticleDOI
07 Apr 2022
TL;DR: It is concluded that collaborating filtering technique used more than all other ones in e-commerce websites and a novel model proposed that fixes the collaborative filtering technique of ‘cold start’ at its best is proposed.
Abstract: There are several benefits of e-commerce websites that include cost effectiveness, convenience, flexibility, fast delivery, increase in income, etc. With these benefits, there is crucial role of e-commerce websites in business and users. However, e-commerce websites produce an overload of data, hence, Recommender Systems (RSs) provides a solution for the data overload problem. The present study, reviews different types of RSs and its pros and cons. Then, it does comparative study of different types of RSs. After the review, it’s concluded that collaborating filtering technique used more than all other ones in e-commerce websites. There are problems with almost all techniques including the collaborative filtering technique too. However there is a novel model proposed that fixes the collaborative filtering technique of ‘cold start’ at its best.

2 citations

References
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Proceedings ArticleDOI
10 May 2005
TL;DR: This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.
Abstract: In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.

1,813 citations

Journal ArticleDOI
TL;DR: Several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
Abstract: In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

648 citations

Book ChapterDOI
14 Sep 2011
TL;DR: A novel method to address the problem of recommending relevant papers to researchers by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size is proposed.
Abstract: With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.

84 citations

Book ChapterDOI
11 Oct 2005
TL;DR: A scalable subspace clustering algorithm (SCuBA) that tackles the large number of dimensions and the sparseness of the data in the research paper domain and performs better than the traditional collaborative filtering approaches when recommending research papers.
Abstract: Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.

81 citations


"A Collaborative Filtering based Rec..." refers background in this paper

  • ...Other recommendation models include subspace-clustering algorithms [5], stereotyping and hybrid recommendations....

    [...]

Journal ArticleDOI
TL;DR: A movie recommendation system based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time.
Abstract: Now a day’s recommendation system has changed the style of searching the things of our interest. This is information filtering approach that is used to predict the preference of that user. The most popular areas where recommender system is applied are books, news, articles, music, videos, movies etc. In this paper we have proposed a movie recommendation system named MOVREC. It is based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time. The recommended movie list is sorted according to the ratings given to these movies by previous users and it uses K-means algorithm for this purpose. MOVREC also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing. This system has been developed in PHP using Dreamweaver 6.0 and Apache Server 2.0. The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases. The user can then browse the recommendations easily and find a movie of their choice.

72 citations


"A Collaborative Filtering based Rec..." refers methods in this paper

  • ...These approaches are combined together to build Hybrid Recommender Systems [1]....

    [...]