Analysis of the Adaptive Nature of Collaborative Filtering Techniques in Dynamic Environment

2015 ◽  
pp. 175-186
Author(s):  
Khaleda Akhter ◽  
Sheikh Muhammad Sarwar
Keyword(s):  
Collaborative Filtering ◽  
Dynamic Environment ◽  
Adaptive Nature ◽  
Filtering Techniques

Healthcare Providers Recommender System Based on Collaborative Filtering Techniques

2022 ◽  
pp. 261-274
Author(s):  
Abdelaaziz Hessane ◽  
Ahmed El Youssefi ◽  
Yousef Farhaoui ◽  
Badraddine Aghoutane ◽  
Noureddine Ait Ali ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Healthcare Providers ◽  
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.

scholarly journals IMPROVING PERSONALIZED QUESTIONNAIRE WITH REDUNDANCY REDUCTION FOR ADDRESSING COLD USER PROBLEM

2021 ◽  
Vol 5 (1) ◽  
pp. 457-466
Author(s):  
Umar Kabiru ◽  
Abubakar Muhammad
Keyword(s):  
Collaborative Filtering ◽  
Performance Improvement ◽  
Online Shopping ◽  
Cold Start ◽  
Reduction Technique ◽  
User Preference ◽  
Redundancy Reduction ◽  
Significant Performance ◽  
Filtering Techniques

User-based and item-based collaborative filtering techniques are among most explored strategies of making products’ recommendations to Users on online shopping platforms. However, a notable weakness of the collaborative filtering techniques is the cold start problem. Which include cold user problem, cold item problem and cold system problem – i.e., the failure of collaborative filtering to make recommendation of products to a new user, failure of an item to be recommended, or combination of the two respectively.  Literature investigation has shown that cold user problem could be effectively addressed using technique of personalized questionnaire. Unfortunately, where the products’ database is too large (as in Amazon.com), results obtained from personalized questionnaire technique could contain some user preference uncertainties. This paper presents technique of improving personalized questionnaire with uncertainty reduction technique. In addition, the paper presents classification of product recommendation systems. In this work we will be limited to user-based cold start.  Experimentation was conducted using Movielens dataset, where the proposed technique achieved significant performance improvement over personalized questionnaire technique with RMSE, Precision, Recall,1 and NDCG of 0.200, 0.227, 0.261, 0.174 and 0.249

An Exploratory Study of Collaborative Filtering Techniques to Analyze the Effect of Information Amount

2017 ◽  
Vol 27 (2) ◽  
pp. 126-138
Author(s):  
Hyun Sil Moon ◽  
◽  
Jung Hyun Yoon ◽  
Il Young Choi ◽  
Jae Kyeong Kim
Keyword(s):  
Collaborative Filtering ◽  
Exploratory Study ◽  
Information Amount ◽  
Filtering Techniques
Sign in / Sign up

Export Citation Format

Share Document