Collaborative Filtering Techniques in Recommendation Systems

2019 ◽  
pp. 11-21 ◽  
Author(s):  
Sandeep K. Raghuwanshi ◽  
R. K. Pateriya
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Filtering Techniques

Collaborative Filtering for Information Recommendation Systems

2011 ◽  
pp. 118-123 ◽  
Author(s):  
Anne Yun-An Chen ◽  
Dennis McLeod
Keyword(s):  
Collaborative Filtering ◽  
Web Sites ◽  
Information Search ◽  
Recommendation Systems ◽  
User Preferences ◽  
Data Sets ◽  
Online Information ◽  
Filtering Algorithms ◽  
User Data ◽  
Filtering Techniques

In order to draw users’ attention and to increase their satisfaction toward online information search results, search-engine developers and vendors try to predict user preferences based on users’ behavior. Recommendations are provided by the search engines or online vendors to the users. Recommendation systems are implemented on commercial and nonprofit Web sites to predict user preferences. For commercial Web sites, accurate predictions may result in higher selling rates. The main functions of recommendation systems include analyzing user data and extracting useful information for further predictions. Recommendation systems are designed to allow users to locate preferable items quickly and to avoid possible information overload. Recommendation systems apply data-mining techniques to determine the similarity among thousands or even millions of data. Collaborative-filtering techniques have been successful in enabling the prediction of user preferences in recommendation systems (Hill, Stead, Rosenstein, & Furnas, 1995, Shardanand & Maes, 1995). There are three major processes in recommendation systems: object data collections and representations, similarity decisions, and recommendation computations. Collaborative filtering aims at finding the relationships among new individual data and existing data in order to further determine their similarity and provide recommendations. How to define the similarity is an important issue. How similar should two objects be in order to finalize the preference prediction? Similarity decisions are concluded differently by collaborative-filtering techniques. For example, people that like and dislike movies in the same categories would be considered as the ones with similar behavior (Chee, Han, & Wang, 2001). The concept of the nearest-neighbor algorithm has been included in the implementation of recommendation systems (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994). The designs of pioneer recommendation systems focus on entertainment fields (Dahlen, Konstan, Herlocker, Good, Borchers, & Riedl, 1998; Resnick et al.; Shardanand & Maes; Hill et al.). The challenge of conventional collaborative-filtering algorithms is the scalability issue (Sarwar, Karypis, Konstan, & Riedl, 2000a). Conventional algorithms explore the relationships among system users in large data sets. User data are dynamic, which means the data vary within a short time period. Current users may change their behavior patterns, and new users may enter the system at any moment. Millions of user data, which are called neighbors, are to be examined in real time in order to provide recommendations (Herlocker, Konstan, Borchers, & Riedl, 1999). Searching among millions of neighbors is a time-consuming process. To solve this, item-based collaborative-filtering algorithms are proposed to enable reductions of computations because properties of items are relatively static (Sarwar, Karypis, Konstan, & Riedl, 2001). Suggest is a top-N recommendation engine implemented with item-based recommendation algorithms (Deshpande & Karypis, 2004; Karypis, 2000). Meanwhile, the amount of items is usually less than the number of users. In early 2004, Amazon Investor Relations (2004) stated that the Amazon.com apparel and accessories store provided about 150,000 items but had more than 1 million customer accounts that had ordered from this store. Amazon.com employs an item-based algorithm for collaborative-filtering-based recommendations (Linden, Smith, & York, 2003) to avoid the disadvantages of conventional collaborative-filtering algorithms.

An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems

2012 ◽  
Vol 38 (3) ◽  
pp. 205-221 ◽  
Author(s):  
Reza Rafeh ◽  
Arash Bahrehmand
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Adaptive Approach ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Filtering Technique ◽  
Over Time ◽  
Filtering Techniques

Recommendation systems manage information overload in order to present personalized content to users based on their interests. One of the most efficient recommendation approaches is collaborative filtering, through which recommendation is based on previously rated data. Collaborative filtering techniques feature impressive solutions for suggesting favourite items to certain users. However, recommendation methods fail to reflect fluctuations in users’ behaviour over time. In this article, we propose an adaptive collaborative filtering algorithm which takes time into account when predicting users’ behaviour. The transitive relationship from one user to another is considered when computing the similarity of two different users. We predict variations of users’ preferences using their profiles. Our experimental results show that the proposed algorithm is more accurate than the classical collaborative filtering technique.

scholarly journals Fashion Recommendation Systems, Models and Methods: A Review

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samit Chakraborty ◽  
Md. Saiful Hoque ◽  
Naimur Rahman Jeem ◽  
Manik Chandra Biswas ◽  
Deepayan Bardhan ◽  
...  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Fast Fashion ◽  
Potential Models ◽  
Scholarly Article ◽  
Enormous Amount ◽  
Shopping Experience ◽  
Tremendous Amount ◽  
Systems Models ◽  
Filtering Techniques

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

A New Collaborative Filtering Approach Based on Game Theory for Recommendation Systems

2021 ◽  
Author(s):  
Selma Benkessirat ◽  
Narhimene Boustia ◽  
Rezoug Nachida
Keyword(s):  
Game Theory ◽  
Collaborative Filtering ◽  
Cooperative Game ◽  
Shapley Value ◽  
Cooperative Game Theory ◽  
Recommendation Systems ◽  
Internet Users ◽  
User Communities ◽  
The One

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

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