Boka et al., 2024 - Google Patents
A survey of sequential recommendation systems: Techniques, evaluation, and future directionsBoka et al., 2024
- Document ID
- 4403121193718776412
- Author
- Boka T
- Niu Z
- Neupane R
- Publication year
- Publication venue
- Information Systems
External Links
Snippet
Recommender systems are powerful tools that successfully apply data mining and machine learning techniques. Traditionally, these systems focused on predicting a single interaction, such as a rating between a user and an item. However, this approach overlooks the …
- 238000000034 method 0 title abstract description 90
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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30522—Query processing with adaptation to user needs
- G06F17/3053—Query processing with adaptation to user needs using ranking
-
- 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
- G06F17/30657—Query processing
- G06F17/3066—Query translation
-
- 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
- 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
- G06F17/30873—Retrieval from the Internet, e.g. browsers by navigation, e.g. using categorized browsing, portals, synchronized browsing, visual networks of documents, virtual worlds or tours
-
- 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
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Boka et al. | A survey of sequential recommendation systems: Techniques, evaluation, and future directions | |
Gabriel De Souza et al. | Contextual hybrid session-based news recommendation with recurrent neural networks | |
Li et al. | Deep learning-based recommendation system: Systematic review and classification | |
Jannach et al. | Recommending based on implicit feedback | |
Kanwal et al. | A review of text-based recommendation systems | |
Lai et al. | An attribute-driven mirror graph network for session-based recommendation | |
Wu et al. | Graph-augmented co-attention model for socio-sequential recommendation | |
Amir et al. | On the current state of deep learning for news recommendation | |
Li et al. | Deep hybrid knowledge graph embedding for top-n recommendation | |
Zhou et al. | Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation | |
Alabduljabbar et al. | Time-aware recommender systems: A comprehensive survey and quantitative assessment of literature | |
Li et al. | Characterizing and predicting downloads in academic search | |
Anwar et al. | CDRec-CAS: cross-domain recommendation using context-aware sequences | |
Liu et al. | Dynamic and static representation learning network for recommendation | |
Gheewala et al. | In-depth survey: deep learning in recommender systems—exploring prediction and ranking models, datasets, feature analysis, and emerging trends | |
Yu et al. | News recommendation model based on encoder graph neural network and bat optimization in online social multimedia art education | |
Peikos et al. | A systematic review of multidimensional relevance estimation in information retrieval | |
Zhou et al. | Attenuated sentiment-aware sequential recommendation | |
Ma et al. | Multi-interest sequential recommendation with contrastive learning and temporal analysis | |
Jiang et al. | A novel high-utility association rule mining method and its application to movie recommendation | |
Zhang et al. | Extract implicit semantic friends and their influences from bipartite network for social recommendation | |
Tang et al. | A feature-aware long-short interest evolution network for sequential recommendation | |
Wang et al. | A sequential recommendation model for balancing long-and short-term benefits | |
Zhang et al. | Adaptive Intention Learning for Session-Based Recommendation | |
Wang et al. | Knowledge graph-aware deep interest extraction network on sequential recommendation |