Shaghayegh Sherry Sahebi, C. Wongchokprasitti, Peter Brusilovsky
The study reported in this paper is an attempt to improve content-based recommendation in CoMeT, a social system for sharing information about research colloquia in Carnegie Mellon and University of Pittsburgh campuses. To improve the quality of recommendation in CoMeT, we explored three additional sources for building user profiles: tags used by users to annotate CoMeT's talks, partial content of CiteULike papers bookmarked by users, and tags used to annotate CiteULike papers. We also compare different tag integration models to study the impact of information fusion on recommendations outcome. The results demonstrate that information encapsulated in CiteULike bookmarks generally helps to improve several aspects of recommendation. The addition of tags by fusing them into keyword profiles helps to improve precision and novelty of recommendation, but may harm systems ability to recommend generally interesting talks. The effects of tags and bookmarks appeared to be stackable.
{"title":"Recommending research colloquia: a study of several sources for user profiling","authors":"Shaghayegh Sherry Sahebi, C. Wongchokprasitti, Peter Brusilovsky","doi":"10.1145/1869446.1869451","DOIUrl":"https://doi.org/10.1145/1869446.1869451","url":null,"abstract":"The study reported in this paper is an attempt to improve content-based recommendation in CoMeT, a social system for sharing information about research colloquia in Carnegie Mellon and University of Pittsburgh campuses. To improve the quality of recommendation in CoMeT, we explored three additional sources for building user profiles: tags used by users to annotate CoMeT's talks, partial content of CiteULike papers bookmarked by users, and tags used to annotate CiteULike papers. We also compare different tag integration models to study the impact of information fusion on recommendations outcome. The results demonstrate that information encapsulated in CiteULike bookmarks generally helps to improve several aspects of recommendation. The addition of tags by fusing them into keyword profiles helps to improve precision and novelty of recommendation, but may harm systems ability to recommend generally interesting talks. The effects of tags and bookmarks appeared to be stackable.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128323677","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}
This paper presents an approach to uniquely identify users and to retrieve their data distributed in profiles stored in different systems. The objective is exploiting the public user data available in the Web and especially in social networks. The approach does not require the implementation of specific protocols and the provision of authentication data. The evaluation provides good results that encourage us in carrying on the extension of the project. The extension we are working on is aimed at aggregating, using heuristic techniques, the data stored in the retrieved profiles and at inferring new data about the user.
{"title":"User data distributed on the social web: how to identify users on different social systems and collecting data about them","authors":"F. Carmagnola, Francesco Osborne, Ilaria Torre","doi":"10.1145/1869446.1869448","DOIUrl":"https://doi.org/10.1145/1869446.1869448","url":null,"abstract":"This paper presents an approach to uniquely identify users and to retrieve their data distributed in profiles stored in different systems. The objective is exploiting the public user data available in the Web and especially in social networks. The approach does not require the implementation of specific protocols and the provision of authentication data. The evaluation provides good results that encourage us in carrying on the extension of the project. The extension we are working on is aimed at aggregating, using heuristic techniques, the data stored in the retrieved profiles and at inferring new data about the user.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127510666","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}
Jon Imanol Durán, J. Laitakari, D. Pakkala, Juho Perälä
User profiles are increasingly used for sharing standard information about users among context-aware agents. User profiles allow agents to offer users personalized content and services. However, the entities and contextual information used by these agents must have same meaning in order to share a common understanding about user related personal information, context and preferences. The contribution of this paper is to present a general user metadata model which is integrated within a generic metadata model (CAM Meta-model) that covers altogether information about content, services, physical and technical environment. This new user profile meta-model has been designed with a view of using it in conjunction with content and service recommender systems. It brings new opportunities to reason over user context data with the main purpose of increasing user experience in ubiquitous environments and satisfying their desires depending on the circumstances.
{"title":"A user meta-model for context-aware recommender systems","authors":"Jon Imanol Durán, J. Laitakari, D. Pakkala, Juho Perälä","doi":"10.1145/1869446.1869456","DOIUrl":"https://doi.org/10.1145/1869446.1869456","url":null,"abstract":"User profiles are increasingly used for sharing standard information about users among context-aware agents. User profiles allow agents to offer users personalized content and services. However, the entities and contextual information used by these agents must have same meaning in order to share a common understanding about user related personal information, context and preferences. The contribution of this paper is to present a general user metadata model which is integrated within a generic metadata model (CAM Meta-model) that covers altogether information about content, services, physical and technical environment. This new user profile meta-model has been designed with a view of using it in conjunction with content and service recommender systems. It brings new opportunities to reason over user context data with the main purpose of increasing user experience in ubiquitous environments and satisfying their desires depending on the circumstances.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513869","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}
We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, we evaluate a number of content-based, collaborative filtering and social recommenders on a heterogeneous dataset obtained from Last.fm. Moreover, aiming to investigate whether and how fusion of such information sources can benefit individual recommendation approaches, we propose various metrics to measure coverage, overlap, diversity and novelty between different sets of recommendations. The obtained results show that, in Last.fm, social tagging and explicit social networking information provide effective and heterogeneous item recommendations. Moreover, they give first insights on the feasibility of exploiting the above non performance recommendation characteristics by hybrid approaches.
{"title":"A study of heterogeneity in recommendations for a social music service","authors":"Alejandro Bellogín, Iván Cantador, P. Castells","doi":"10.1145/1869446.1869447","DOIUrl":"https://doi.org/10.1145/1869446.1869447","url":null,"abstract":"We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, we evaluate a number of content-based, collaborative filtering and social recommenders on a heterogeneous dataset obtained from Last.fm. Moreover, aiming to investigate whether and how fusion of such information sources can benefit individual recommendation approaches, we propose various metrics to measure coverage, overlap, diversity and novelty between different sets of recommendations. The obtained results show that, in Last.fm, social tagging and explicit social networking information provide effective and heterogeneous item recommendations. Moreover, they give first insights on the feasibility of exploiting the above non performance recommendation characteristics by hybrid approaches.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128233731","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}
Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's specific context and to the changing shape of user interests over time. Our approach to building context-sensitive collaborative recommendation is a hybrid one that incorporates semantic knowledge in the form of a domain ontology. User profiles are defined relative to the ontology, giving rise to an ontological user profile. In this paper, we describe how ontological user profiles are learned, incrementally updated, and used for collaborative recommendation. Using book rating data, we demonstrate that this recommendation algorithm offers improved coverage, diversity, personalization, and cold-start performance while at the same time enhancing recommendation accuracy.
{"title":"Improving the effectiveness of collaborative recommendation with ontology-based user profiles","authors":"A. Sieg, B. Mobasher, R. Burke","doi":"10.1145/1869446.1869452","DOIUrl":"https://doi.org/10.1145/1869446.1869452","url":null,"abstract":"Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's specific context and to the changing shape of user interests over time. Our approach to building context-sensitive collaborative recommendation is a hybrid one that incorporates semantic knowledge in the form of a domain ontology. User profiles are defined relative to the ontology, giving rise to an ontological user profile. In this paper, we describe how ontological user profiles are learned, incrementally updated, and used for collaborative recommendation. Using book rating data, we demonstrate that this recommendation algorithm offers improved coverage, diversity, personalization, and cold-start performance while at the same time enhancing recommendation accuracy.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116496368","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}
B. Heitmann, J. G. B. Kim, Alexandre Passant, Conor Hayes, H. Kim
Providing relevant recommendations requires access to user profile data. Current social networking ecosystems allow third party services to request user authorisation for accessing profile data, thus enabling cross-domain recommendation. However these ecosystems create user lock-in and social networking data silos, as the profile data is neither portable nor interoperable. We argue that innovations in reconciling heterogeneous data sources must be also be matched by innovations in architecture design and recommender methodology. We present and qualitatively evaluate an architecture for privacy-enabled user profile portability, which is based on technologies from the emerging Web of Data (FOAF, WebIDs and the Web Access Control vocabulary). The proposed architecture enables the creation of a universal "private by default" ecosystem with interoperability of user profile data. The privacy of the user is protected by allowing multiple data providers to host their part of the user profile. This provides an incentive for more users to make profile data from different domains available for recommendations.
{"title":"An architecture for privacy-enabled user profile portability on the web of data","authors":"B. Heitmann, J. G. B. Kim, Alexandre Passant, Conor Hayes, H. Kim","doi":"10.1145/1869446.1869449","DOIUrl":"https://doi.org/10.1145/1869446.1869449","url":null,"abstract":"Providing relevant recommendations requires access to user profile data. Current social networking ecosystems allow third party services to request user authorisation for accessing profile data, thus enabling cross-domain recommendation. However these ecosystems create user lock-in and social networking data silos, as the profile data is neither portable nor interoperable. We argue that innovations in reconciling heterogeneous data sources must be also be matched by innovations in architecture design and recommender methodology. We present and qualitatively evaluate an architecture for privacy-enabled user profile portability, which is based on technologies from the emerging Web of Data (FOAF, WebIDs and the Web Access Control vocabulary). The proposed architecture enables the creation of a universal \"private by default\" ecosystem with interoperability of user profile data. The privacy of the user is protected by allowing multiple data providers to host their part of the user profile. This provides an incentive for more users to make profile data from different domains available for recommendations.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134408116","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}
Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.
{"title":"Comparison of implicit and explicit feedback from an online music recommendation service","authors":"Gawesh Jawaheer, M. Szomszor, P. Kostkova","doi":"10.1145/1869446.1869453","DOIUrl":"https://doi.org/10.1145/1869446.1869453","url":null,"abstract":"Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"22 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641513","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}
We propose a geographical information recommender system based on interaction between user's map operation and category selection. The system has three interfaces, the layered category interface, the geographical object interface and the digital map interface. Our system interactively updates each interface based on the category interest model and the region interest model. This paper describes each interface and each model, and how to update them by our system.
{"title":"Geographical recommender system based on interaction between map operation and category selection","authors":"Kenta Oku, Rika Kotera, K. Sumiya","doi":"10.1145/1869446.1869458","DOIUrl":"https://doi.org/10.1145/1869446.1869458","url":null,"abstract":"We propose a geographical information recommender system based on interaction between user's map operation and category selection. The system has three interfaces, the layered category interface, the geographical object interface and the digital map interface. Our system interactively updates each interface based on the category interest model and the region interest model. This paper describes each interface and each model, and how to update them by our system.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195876","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}
P. Lops, C. Musto, F. Narducci, Marco de Gemmis, Pierpaolo Basile, G. Semeraro
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems. In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. As a consequence, the recommender system is able to suggest items represented in a language different from the one used in the content-based user profile. Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.
{"title":"MARS: a MultilAnguage Recommender System","authors":"P. Lops, C. Musto, F. Narducci, Marco de Gemmis, Pierpaolo Basile, G. Semeraro","doi":"10.1145/1869446.1869450","DOIUrl":"https://doi.org/10.1145/1869446.1869450","url":null,"abstract":"The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems.\u0000 In this paper, we propose a language-independent content-based recommender system, called MARS (MultilAnguage Recommender System), that builds cross-language user profiles, by shifting the traditional text representation based on keywords, to a more complex language-independent representation based on word meanings. As a consequence, the recommender system is able to suggest items represented in a language different from the one used in the content-based user profile.\u0000 Experiments conducted in a movie recommendation scenario show the effectiveness of the approach.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128138890","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}
Luis Martínez Marina, Juan Antonio Recio García, Estefanía Martín-Barroso
The number of people that use Internet as a source of information increases continuously. Internet provides a great amount of heterogeneous information. When interacting with the Web, not all the users have the same goals, interests or needs. This paper presents a recommender system for spare time activities (such as visiting museums, restaurants, conferences, etc.). It suggests the most suitable options taking into account the personal features of each user, that is, his/her preferences, economic resources, available time and disabilities. Furthermore, it provides the means of public transport to arrive at the place where the activity will be performed. The results of a case study focused on Mostoles city are presented too.
{"title":"Ontology-based web service to recommend spare time activities","authors":"Luis Martínez Marina, Juan Antonio Recio García, Estefanía Martín-Barroso","doi":"10.1145/1869446.1869457","DOIUrl":"https://doi.org/10.1145/1869446.1869457","url":null,"abstract":"The number of people that use Internet as a source of information increases continuously. Internet provides a great amount of heterogeneous information. When interacting with the Web, not all the users have the same goals, interests or needs. This paper presents a recommender system for spare time activities (such as visiting museums, restaurants, conferences, etc.). It suggests the most suitable options taking into account the personal features of each user, that is, his/her preferences, economic resources, available time and disabilities. Furthermore, it provides the means of public transport to arrive at the place where the activity will be performed. The results of a case study focused on Mostoles city are presented too.","PeriodicalId":258506,"journal":{"name":"HetRec '10","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130512969","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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