Since the advent of the Web 2.0, the amount of digital data available became increasingly overwhelming for a user looking for specific information. As a consequence, personalisation systems aiming at assisting the user in this task have emerged. The use of semantic web technologies to represent user profiles and their interests has shown some promising results allowing to infer preferences not directly gathered via explicit or implicit profiling. On the other hand, linguistic values, often conveniently used by humans when expressing their tastes or preferences, are another way to provide richer representation of user preferences. The aim of this paper is to propose a combination of semantic user modelling and linguistics values, and to show how a recommender system could benefit from this representation. To achieve this objective, we first propose an integrated semantic user model based on FOAF, permitting the expression of contextualised and weighted interests. An integration of linguistic values within this user model is then exemplified and, finally, we also propose an aggregation method to exploit linguistic values in recommender systems.
{"title":"Combining Linguistic Values and Semantics to Represent User Preferences","authors":"V. Grouès, Y. Naudet, O. Kao","doi":"10.1109/SMAP.2011.21","DOIUrl":"https://doi.org/10.1109/SMAP.2011.21","url":null,"abstract":"Since the advent of the Web 2.0, the amount of digital data available became increasingly overwhelming for a user looking for specific information. As a consequence, personalisation systems aiming at assisting the user in this task have emerged. The use of semantic web technologies to represent user profiles and their interests has shown some promising results allowing to infer preferences not directly gathered via explicit or implicit profiling. On the other hand, linguistic values, often conveniently used by humans when expressing their tastes or preferences, are another way to provide richer representation of user preferences. The aim of this paper is to propose a combination of semantic user modelling and linguistics values, and to show how a recommender system could benefit from this representation. To achieve this objective, we first propose an integrated semantic user model based on FOAF, permitting the expression of contextualised and weighted interests. An integration of linguistic values within this user model is then exemplified and, finally, we also propose an aggregation method to exploit linguistic values in recommender systems.","PeriodicalId":346975,"journal":{"name":"2011 Sixth International Workshop on Semantic Media Adaptation and Personalization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133535710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we analyze large user photo collections from Flickr in order to select the most appropriate tags to describe a geographical area. We cluster photos based on their latitude and longitude and divide large areas into smaller clusters, which we will refer to as "geo-clusters". Geo-clusters have a fixed size and are able to overlap. They do not cover the entire area of interest, omitting parts where no single photo has been geo-tagged at. Within each geo-cluster we analyze all collected textual metadata i.e. the user selected tags of the photos it contains. We are then able to rank them and select the most appropriate that are able to describe landmarks and other places of interest that are contained within. Finally we place these tags on a map to help users to intuitively understand places of interest/visual content at a glance.
{"title":"Placing User-Generated Photo Metadata on a Map","authors":"E. Spyrou, Phivos Mylonas","doi":"10.1109/SMAP.2011.16","DOIUrl":"https://doi.org/10.1109/SMAP.2011.16","url":null,"abstract":"In this paper we analyze large user photo collections from Flickr in order to select the most appropriate tags to describe a geographical area. We cluster photos based on their latitude and longitude and divide large areas into smaller clusters, which we will refer to as \"geo-clusters\". Geo-clusters have a fixed size and are able to overlap. They do not cover the entire area of interest, omitting parts where no single photo has been geo-tagged at. Within each geo-cluster we analyze all collected textual metadata i.e. the user selected tags of the photos it contains. We are then able to rank them and select the most appropriate that are able to describe landmarks and other places of interest that are contained within. Finally we place these tags on a map to help users to intuitively understand places of interest/visual content at a glance.","PeriodicalId":346975,"journal":{"name":"2011 Sixth International Workshop on Semantic Media Adaptation and Personalization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125124847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. I. Martín-Vicente, A. Gil-Solla, M. Cabrer, Y. Blanco-Fernández, Martín López Nores
E-commerce has become a major application domain for recommender systems due to its business interest. These tools aim to identify the products each user may like or find useful, which can boost users' consumption. Particularly, collaborative recommender systems rely on a set of like-minded users to select the products to offer. Taking into account the expertise of the users who drive such decision can increase the accuracy of the process. However, current approaches require extra data, that is not often available, to obtain expertise measures. In this paper, we apply a semantic approach to get a measure of practical expertise by exploiting the data available in any e-commerce recommender system-the consumption histories of the users. This way, we improve recommendation results transparently to the users.
{"title":"Improving e-Commerce Collaborative Recommendations by Semantic Inference of Neighbors' Practical Expertise","authors":"M. I. Martín-Vicente, A. Gil-Solla, M. Cabrer, Y. Blanco-Fernández, Martín López Nores","doi":"10.1109/SMAP.2011.12","DOIUrl":"https://doi.org/10.1109/SMAP.2011.12","url":null,"abstract":"E-commerce has become a major application domain for recommender systems due to its business interest. These tools aim to identify the products each user may like or find useful, which can boost users' consumption. Particularly, collaborative recommender systems rely on a set of like-minded users to select the products to offer. Taking into account the expertise of the users who drive such decision can increase the accuracy of the process. However, current approaches require extra data, that is not often available, to obtain expertise measures. In this paper, we apply a semantic approach to get a measure of practical expertise by exploiting the data available in any e-commerce recommender system-the consumption histories of the users. This way, we improve recommendation results transparently to the users.","PeriodicalId":346975,"journal":{"name":"2011 Sixth International Workshop on Semantic Media Adaptation and Personalization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129273992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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