scholarly journals Improving Recommendations for Online Retail Markets Based on Ontology Evolution

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1650
Author(s):  
Rana Alaa ◽  
Mariam Gawish ◽  
Manuel Fernández-Veiga
Keyword(s):  
Semantic Web ◽  
Recommendation System ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Purchase Decisions ◽  
Online Retail ◽  
Ontology Evolution ◽  
The One ◽  
Machine Readable ◽  
Machine Readable Form

The semantic web is considered to be an extension of the present web. In the semantic web, information is given with well-defined meanings, and thus helps people worldwide to cooperate together and exchange knowledge. The semantic web plays a significant role in describing the contents and services in a machine-readable form. It has been developed based on ontologies, which are deemed the backbone of the semantic web. Ontologies are a key technique with which semantics are annotated, and they provide common comprehensible foundation for resources on the semantic web. The use of semantics and artificial intelligence leads to what is known to be “Smarter Web”, where it will be easy to retrieve what customers want to see on e-commerce platforms, and thus will help users save time and enhance their search for the products they need. The semantic web is used as well as webs 3.0, which helps enhancing systems performance. Previous personalized recommendation methods based on ontologies identify users’ preferences by means of static snapshots of purchase data. However, as the user preferences evolve with time, the one-shot ontology construction is too constrained for capturing individual diverse opinions and users’ preferences evolution over time. This paper will present a novel recommendation system architecture based on ontology evolution, the proposed subsystem architecture for ontology evolution. Furthermore, the paper proposes an ontology building methodology based on a semi-automatic technique as well as development of online retail ontology. Additionally, a recommendation method based on the ontology reasoning is proposed. Based on the proposed method, e-retailers can develop a more convenient product recommendation system to support consumers’ purchase decisions.

The relevance of terminologies for automatic abstracting

1982 ◽  
Vol 4 (4) ◽  
pp. 161-165
Author(s):  
Wolfgang Nedobity
Keyword(s):  
Scientific Literature ◽  
Relevant Information ◽  
Automatic Production ◽  
Readable Form ◽  
Automatic Abstracting ◽  
The One ◽  
Machine Readable ◽  
Machine Readable Form

The increased production and publication of professional and scientific literature makes it necessary that abstracts are pro duced in a quick, efficient and economical way. This can be achieved by the mechanization of abstracting. With the aid of computers, extracts can be produced of all kinds of texts which are available in machine-readable form. The main problem of this procedure is how to determine the key sentences of a text, i.e., the passages that contain the most relevant information. Various methods have been developed for this purpose; the one presented here is based on the fact that in order to convey relevant information, subject terminology is used. In many cases subject terminologies are now available in machine-reada ble form too and thus can be easily applied to the automatic production of abstracts.

scholarly journals Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

2022 ◽  
Vol 34 (3) ◽  
pp. 1-21
Author(s):  
Xue Yu
Keyword(s):  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Target Location ◽  
Geographical Location ◽  
Cold Start ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Interest Point ◽  
Source Information ◽  
Interest Points

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

scholarly journals A User Preference Tree based Personalized Route Recommendation System for Constraint Tourism and Travel

2021 ◽  
Author(s):  
Kanimozhi U ◽  
Sannasi Ganapathy ◽  
Manjula D ◽  
Arputharaj Kannan
Keyword(s):  
Social Media ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preferences ◽  
Data Sources ◽  
Personalized Recommendation ◽  
Tourist Destination ◽  
Cost Constraints ◽  
Heterogeneous Data Sources ◽  
Route Recommendation

Abstract Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses User-Location Vector with LDA and Jaccard Coefficients. Moreover, a new Tourist Destination tree is constructed using the posts extracted from TripAdvisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based Travelling Salesman Problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best Tourist Destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed User Centric Personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.

scholarly journals Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

2017 ◽  
Author(s):  
Sara Saeedi ◽  
Xueyang Zou ◽  
Mariel Gonzales ◽  
Steve Liang
Keyword(s):  
Web Application ◽  
Management Practices ◽  
Recommendation System ◽  
Contextual Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Context Information ◽  
Context Aware ◽  
Context Reasoning ◽  
Past Behavior

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Ontology-Based Database Approach for Handling Preferences

2010 ◽  
pp. 248-271 ◽  
Author(s):  
Dilek Tapucu ◽  
Gayo Diallo ◽  
Yamine Ait Ameur ◽  
Murat Osman Ünalir
Keyword(s):  
Information Systems ◽  
Semantic Web ◽  
User Preferences ◽  
Generic Model ◽  
Information Searching ◽  
Modelling Language ◽  
Preference Model ◽  
Proposed Model ◽  
The One

Information systems now manage huge amount of data. Users are overwhelmed by the numerous results provided in response to their requests. These results must often be sorted and filtered in order to be usable. Moreover, the “one size fits all” approach has shown its limitation for information searching in many applications, particularly in the e-commerce domain. The capture and exploitation of user preferences have been proposed as a solution to overcome this problem. However, the existing approaches usually define preferences for a particular application. Thus, it is difficult to share and reuse the handled preferences in other contexts. In this chapter, we propose a sharable, formal and generic model to represent user‘s preferences. The model gathers several preferences models proposed in the Database and Semantic Web communities. The novelty of our approach is that the defined preferences are attached to the ontologies which describe the semantic of the data manipulated by the applications. Moreover, the proposed model offers a persistence mechanism and a dedicated language; it is implemented using Ontology-Based Databases (OBDB) system extended in order to take into account preferences. OBDB manage both ontologies and the data instances. The preference model is formally defined using theEXPRESS data modelling language which ensures us a free ambiguity definition and the approach is illustrated through a case study in the tourism domain.

Meta-Rule Based Recommender Systems for Educational Applications

2012 ◽  
pp. 211-231 ◽  
Author(s):  
Vicente Arturo Romero Zaldivar ◽  
Daniel Burgos ◽  
Abelardo Pardo
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Rule Based ◽  
User Data ◽  
Rule Sets ◽  
Rule Set ◽  
Educational Applications

Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.

scholarly journals X-Similarity Comparison by using Wordnet

2017 ◽  
Vol 1 (4-2) ◽  
pp. 188
Author(s):  
Shahreen Kasim ◽  
Nurul Aswa Omar ◽  
Nurul Suhaida Mohammad Akbar ◽  
Rohayanti Hassan ◽  
Masrah Azrifah Azmi Murad
Keyword(s):  
Semantic Web ◽  
Semantic Similarity ◽  
Semantic Description ◽  
Similarity Comparison ◽  
Readable Form ◽  
Feature Based ◽  
Machine Readable ◽  
Machine Readable Form

Semantic web is an addition of the previous one that represents information more significantly for humans and computers. It enables the description of contents and services in machine readable form. It also enables annotating, discovering, publishing, advertising and composing services to be programmed. Semantic web was developed based on Ontology which is measured as the backbone of the semantic web. Machine-readable is transformed to machine-understandable in the current web. Moreover, Ontology provides a common vocabulary, a grammar for publishing data and can provide a semantic description of data which can be used to conserve the Ontology and keep them ready for implication. There are many that used in feature based in semantic similarity. This research presents a single ontology of X-Similarity feature based method.

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