Tyagi et al., 2012 - Google Patents
Enhanced new user recommendations based on quantitative association rule miningTyagi et al., 2012
View PDF- Document ID
- 890867916336938566
- Author
- Tyagi S
- Bharadwaj K
- Publication year
- Publication venue
- Procedia Computer Science
External Links
Snippet
In the era of information explosion, how to provide tailored suggestions to a new user is a major concern for collaborative filtering (CF) based recommender systems. The CF recommender system performs very poorly for a new user with very poor profile information …
- 238000005065 mining 0 title description 19
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/30707—Clustering or classification into predefined classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
- G06Q30/0203—Market surveys or market polls
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30017—Multimedia data retrieval; Retrieval of more than one type of audiovisual media
- G06F17/30023—Querying
- G06F17/30029—Querying by filtering; by personalisation, e.g. querying making use of user profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0241—Advertisement
- G06Q30/0251—Targeted advertisement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/06—Buying, selling or leasing transactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ganu et al. | Improving the quality of predictions using textual information in online user reviews | |
| Li et al. | Exploiting explicit and implicit feedback for personalized ranking | |
| Zheng et al. | Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach | |
| Chatti et al. | Tag-based collaborative filtering recommendation in personal learning environments | |
| Sarwar | Sparsity, scalability, and distribution in recommender systems | |
| Gatzioura et al. | A case-based recommendation approach for market basket data | |
| Domingues et al. | Dimensions as virtual items: Improving the predictive ability of top-n recommender systems | |
| US8825641B2 (en) | Measuring duplication in search results | |
| Salehi | An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product | |
| Liu et al. | Fast recommendation on latent collaborative relations | |
| Heredia et al. | Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection | |
| Sánchez-Moreno et al. | A Session‐Based Song Recommendation Approach Involving User Characterization along the Play Power‐Law Distribution | |
| Xu et al. | Leveraging app usage contexts for app recommendation: a neural approach | |
| Mirza | Jumping connections: A graph-theoretic model for recommender systems | |
| Tyagi et al. | Enhanced new user recommendations based on quantitative association rule mining | |
| Salehi et al. | A hybrid recommendation approach based on attributes of products using genetic algorithm and naive Bayes classifier | |
| Tsapatsoulis et al. | Investigating the scalability of algorithms, the role of similarity metric and the list of suggested items construction scheme in recommender systems | |
| Ziegler et al. | On exploiting classification taxonomies in recommender systems | |
| Saha et al. | Predicting preference tags to improve item recommendation | |
| Jethava et al. | Scalable multi-dimensional user intent identification using tree structured distributions | |
| Coba et al. | Replicating and improving Top-N recommendations in open source packages | |
| Saia et al. | A latent semantic pattern recognition strategy for an untrivial targeted advertising | |
| Haque et al. | Divergence based non-negative matrix factorization for top-N recommendations | |
| Ganesan et al. | A comprehensive survey on recommender system techniques | |
| Elkhelifi et al. | Recommendation Systems Based on Online User's Action |