scholarly journals A Research on Similarity Measure to Identify Effective Similar Users in Recommender Systems

2019 ◽  
Vol 8 (9S2) ◽  
pp. 834-840
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Drastic Increase ◽  
Measure Model ◽  
Filtering Technique ◽  
Selection Of

In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

2021 ◽  
Vol 10 (3) ◽  
pp. 727
Author(s):  
Tajul Rosli Razak ◽  
Mohammad Hafiz Ismail ◽  
Shukor Sanim Mohd Fauzi ◽  
Ray Adderley JM Gining ◽  
Ruhaila Maskat
Keyword(s):  
Artificial Intelligence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Real World ◽  
Participatory Design ◽  
Design Methodology ◽  
Multiple Correlation

<span lang="EN-GB">A recommender system is an algorithm aiming at giving suggestions to users on relevant elements or items such as products to purchase, books to read, jobs to apply or anything else depending on industries or situations. Recently, there has been a surge in interest in developing a recommender system in a variety of areas. One of the most widely used approaches in recommender systems is collaborative filtering (CF). The CF is a strategy for automatically creating a filter based on a user's needs by extracting desires or recommendation information from a large number of users. The CF approach uses multiple correlation steps to do this. However, the occurrence of uncertainty in finding the best similarity measure is unavoidable. This paper outlines a method for improving the configuration of a recommender system that is tasked with recommending an appropriate study field and supervisor to a group of final-year project students. The framework we suggest is built on a participatory design methodology that allows students' individual opinions to be factored into the recommender system's design. The architecture of the recommender scheme was also illustrated using a real-world scenario, namely mapping the students' field of interest to a possible supervisor for the final year project.</span>

scholarly journals A User- Based Recommendation with a Scalable Machine Learning Tool

2015 ◽  
Vol 5 (5) ◽  
pp. 1153 ◽  
Author(s):  
Ch. Veena ◽  
B. Vijaya Babu
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Open Source ◽  
Recommender Systems ◽  
Recommender System ◽  
Historical Data ◽  
Machine Learning Algorithms ◽  
Frame Work ◽  
Machine Learning Tool ◽  
Selection Of

Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.

scholarly journals Recommender system for surplus stock clearance

2019 ◽  
Vol 9 (5) ◽  
pp. 3813
Author(s):  
Vipul Agarwal ◽  
Vijayalakshmi A
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
Similarity Metrics ◽  
Manual Work ◽  
Similarity Metric ◽  
Filtering Technique ◽  
Retail Shop ◽  
Item Item

Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend the stock which requires clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit

scholarly journals A Movie Recommender System using Ontology Based Semantic Similarity Measure

2019 ◽  
Vol 8 (12) ◽  
pp. 5490-5496
Keyword(s):  
Recommender Systems ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Recommender System ◽  
Domain Knowledge ◽  
Information Overload ◽  
Vital Role ◽  
The Internet ◽  
Semantic Similarity Measure ◽  
Usage Data

Continuous growth in information available on the Internet overwhelms the users during navigation. This information overload may result in users’ dissatisfaction which is undesirable. Users’ satisfaction is very important aspect in every domain. Recommender systems play a vital role in dealing with information overload problems. The recommender systems filter the huge information on the Internet to generate limited and personalized information to users. This helps in increasing users' satisfaction by retaining his/her interests during navigation. Pure Web usage data based recommender systems have been used from last few years. However, they lag in precise recommendations because of absence of domain knowledge. Further, the similarity measures play a vital role in recommendation process and hence affect the performance of the recommender systems. The performance of recommender systems can be enhanced through integration of domain knowledge with usage data. This paper presents an approach to movie recommender system that integrates domain knowledge with usage data. The ontology is used to represent domain knowledge. The proposed approach is based on a new ontology based semantic similarity measure. The experimental results prove that the recommendations’ quality andaccuracy of prediction can be enhanced through integration of ontological domain knowledge with Web usage data.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

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