A New Semantic Similarity Measure Based On Ontology for Movie Rate Prediction

A recommendation algorithm comprises of two important steps: 1) Predicting rates, and 2) Recommendation. Rate prediction is a cumulative function of the similarity score between two movies and rate history of those movies by other users. There are various methods for rate prediction such as weighted sum method, regression, deviation based etc. All these methods rely on finding similar items to the items previously viewed/rated by target user, with assumption that user tends to have similar rating for similar items. Computing the similarities can be done using various similarity measures such as Euclidian Distance, Cosine Similarity, Adjusted Cosine Similarity, Pearson Correlation, Jaccard Similarity etc. All of these well-known approaches calculate similarity score between two movies using simple rating based data. Hence, such similarity measures could not accurately model rating behavior of user. In this paper, we will show that the accuracy in rate prediction can be enhanced by incorporating ontological domain knowledge in similarity computation. This paper introduces a new ontological semantic similarity measure between two movies. For experimental evaluation, the performance of proposed approach is compared with two existing approaches: 1) Adjusted Cosine Similarity (ACS), and 2) Weighted Slope One (WSO) algorithm, in terms of two performance measures: 1) Execution time and 2) Mean Absolute Error (MAE). The open-source Movielens (ml-1m) dataset is used for experimental evaluation. As our results show, the ontological semantic similarity measure enhances the performance of rate prediction as compared to the existing-well known approaches.

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Toward semantic similarity measure between concepts in an ontology

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1356-1372 ◽

2019 ◽

Vol 14 (3) ◽

pp. 1356

Author(s):

Suwan Tongphu

Keyword(s):

Semantic Similarity ◽

Similarity Measure ◽

Description Logic ◽

Classical Problem ◽

Similarity Score ◽

New Method ◽

Snomed Ct ◽

Major Drawback ◽

Semantic Similarity Measure ◽

Concept Definition

<p>A similarity measure is one classical problem in Description Logic which aims at identifying the similarity between concepts in an ontology. Finding a hierarchy distance among concepts in an ontology is one popular technique. However, one major drawback of such a technique is that it usually ignores a concept definition analysis. This work introduces a new method for similarity measure. The proposed system semantically analyzes structures of two concept descriptions and then computes the similarity score based on the number of shared features. The efficiency of the proposed algorithm is measured by means of the satisfaction of desirable properties and intensive experiments on the Snomed ct ontology.</p>

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Knowledge-based sentence semantic similarity: algebraical properties

Progress in Artificial Intelligence ◽

10.1007/s13748-021-00248-0 ◽

2021 ◽

Author(s):

Mourad Oussalah ◽

Muhidin Mohamed

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Semantic Similarity ◽

Language Processing ◽

Similarity Measures ◽

Canonical Extension ◽

Similarity Score ◽

Semantic Similarity Measure ◽

Sentence Similarity

AbstractDetermining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.

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EMD Based Semantic User Similarity using Past Travel Histories

Journal of Cases on Information Technology ◽

10.4018/jcit.20220801oa04 ◽

2022 ◽

Vol 24 (3) ◽

pp. 0-0

Keyword(s):

Information Retrieval ◽

Mobile Devices ◽

Semantic Similarity ◽

Similarity Measure ◽

Similarity Measures ◽

Cost Effective ◽

Semantic Similarity Measure ◽

User Similarity ◽

Percentage Improvement ◽

The Cost

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

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A Combinative Similarity Computing Measure for Collaborative Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.2919 ◽

2013 ◽

Vol 347-350 ◽

pp. 2919-2925 ◽

Cited By ~ 2

Author(s):

Lin Guo ◽

Qin Ke Peng

Keyword(s):

Collaborative Filtering ◽

Similarity Measure ◽

Pearson Correlation ◽

Similarity Measures ◽

Cosine Similarity ◽

Computation Complexity ◽

Satisfactory Performance ◽

Similarity Method

Similarity method is the key of the user-based collaborative filtering recommend algorithm. The traditional similarity measures, which cosine similarity, adjusted cosine similarity and Pearson correlation similarity are included, have some advantages such as simple, easy and fast, but with the sparse dataset they may lead to bad recommendation quality. In this article, we first research how the recommendation qualities using the three similarity methods respectively change with the different sparse datasets, and then propose a combinative similarity measure considering the account of items users co-rated. Compared with the three algorithms, our method shows its satisfactory performance with the same computation complexity.

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