Back to Search
Start Over
Quantifying textual terms of items for similarity measurement
- Source :
- Information Sciences. :269-282
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- It is well known that recommender systems rely on the similarity between items to be recommended. Most current research projects in this area utilize traditional similarity measurement algorithms, such as cosine distance or derivatives of these. However, the most challenging problem facing these approaches is to quantify the non-numerical attributes of items. This is quite intractable and cannot be solved with regular similarity measurement algorithms. This paper proposes two novel methods, the Taxonomic Trees Similarity Measurement (TTSM) and the Decomposed Structures Similarity Measurement (SDSM), so that the similarities between the textual attributes can be measured using numeric values after they have been quantified. Also, the quantifying process is completely based on the semantic meanings of the textual terms. Furthermore, a maximized term matching (MTM) mechanism is induced and applied to the group-based textual attributes of items in recommender systems. Finally, we evaluate our methods by implementing a recipe recommender system which achieves a 74.4% overall satisfaction rate as evaluated by real users.
- Subjects :
- Matching (statistics)
Information Systems and Management
Information retrieval
Computer science
Process (engineering)
02 engineering and technology
Recommender system
computer.software_genre
Computer Science Applications
Theoretical Computer Science
Term (time)
Similarity (network science)
Artificial Intelligence
Control and Systems Engineering
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Data mining
computer
Software
Subjects
Details
- ISSN :
- 00200255
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........3d83a8d4d09cb539c261dba7a359b580