An Implementation and Combining of Hybrid and Content Based and Collaborative Filtering Algorithms for the Higher Performance of Recommended Sytems

2021 ◽  
pp. 99-111
Author(s):  
B. Geluvaraj ◽  
Meenatchi Sundaram
Keyword(s):  
Collaborative Filtering ◽  
Filtering Algorithms

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.

New SVD-based collaborative filtering algorithms with differential privacy

2017 ◽  
Vol 33 (4) ◽  
pp. 2133-2144 ◽  
Author(s):  
Zhengzheng Xian ◽  
Qiliang Li ◽  
Xiaoyu Huang ◽  
Lei Li
Keyword(s):  
Collaborative Filtering ◽  
Differential Privacy ◽  
Filtering Algorithms

Examining collaborative filtering algorithms for clothing recommendation in e-commerce

2018 ◽  
Vol 89 (14) ◽  
pp. 2821-2835 ◽  
Author(s):  
Zhi-Hua Hu ◽  
Xiang Li ◽  
Chen Wei ◽  
Hong-Lei Zhou
Keyword(s):  
Collaborative Filtering ◽  
Filtering Algorithms

Towards a More Comprehensive Comparison of Collaborative Filtering Algorithms

2002 ◽  
pp. 248-253 ◽  
Author(s):  
Cristina N. González-Caro ◽  
Maritza L. Calderón-Benavides ◽  
José de J. Pérez-Alcázar ◽  
Juan C. García-Díaz ◽  
Joaquin Delgado
Keyword(s):  
Collaborative Filtering ◽  
Filtering Algorithms ◽  
Comprehensive Comparison

Study on Recommendation Method Based on Product Evaluation Concept Tree and Collaborative Filtering Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 401-404
Author(s):  
Mei Hao ◽  
Bo Qing Zhang
Keyword(s):  
Collaborative Filtering ◽  
Product Evaluation ◽  
Tablet Pc ◽  
Experiment Data ◽  
Raw Data ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Data Sparseness ◽  
Similar Product ◽  
Collaborative Filtering Algorithm

In view of the data sparseness of traditional collaborative filtering algorithms, this paper introduces product evaluation concept tree to optimize the calculation of similarity, and uses the concepts similarity replace the items similarity. The hypothesis of this new algorithm is that the customers tend to purchase products according with themselves. So if a customer has selected a product, then he or she is more likely to choose a similar product. Finally, we implement this algorithms improvement by c#. Experimental raw data is got from tablet PC reviews of JingDong Mall. We process the product feature scores and get the recommendation results based on the reviews mining. The experiment data proves that the recommendation result is reasonable.

scholarly journals I’m Like You, Just Not In That Way: Tag Networks to Improve Collaborative Filtering

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 95
Author(s):  
Jason Boorn ◽  
Debra S Goldberg
Keyword(s):  
Collaborative Filtering ◽  
The Other ◽  
Online Content ◽  
Filtering Algorithms

Collaborative filtering aims to predict a person’s preferences by examining the preferences of similar people. Many collaborative filtering algorithms rely on a coarse notion of similarity, which assumes that if two people are sufficiently simiar in a few specific areas, each is likely to make good recommendations for the other in most areas. Our trust in the opinions of others, though, is rarely absolute; we often tend to trust recommendations from certain people in certain areas. In this paper we develop an algorithm which reflects this notion. Rather than capturing taste information at the user level, we capture taste at the topic level by making use of tags: arbitrary words or phrases which are often used to group online content. Previous attempts to improve collaborative filtering using tag information have attempted to determine tag meanings, and as a result have depended upon complex semantic analyses. Our algorithm avoids these complications by focusing instead on the clusters which tags establish. Using tags in this way provides a significant improvement in the accuracy of recommendations without a commensurate loss in coverage. These tag clusters also give rise to networks which can be exploited to further improve recommendation results.

Sign in / Sign up

Export Citation Format

Share Document