Dynamically Personalizing Search Results for Mobile Users

Bouidghaghen, Ourdia; Tamine-Lechani, Lynda

doi:10.1007/978-3-642-04957-6_9

Cited by 11 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…X i j ) is the value of the j th feature of the situation vector S * (resp. S i ), simj is the similarity metric related to the j th feature of a situation vector and αj its associated weight as defined in our previous work [1].…”

Section: Retrieve the Most Similar Situationmentioning

confidence: 99%

Inferring situation-based interests for mobile users

Bouidghaghen

Tamine

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Retrieve the Most Similar Situationmentioning

confidence: 99%

Inferring situation-based interests for mobile users

Bouidghaghen

Tamine

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bouidghaghen et al [17] propose to extend OWL-Time ontology with the following classes: time of day, day of week and season. Indeed, they introduce a situationaware personalised search approach, where: (i) semantic (i.e., high-level) situations are inferred from low-level location and time data, (ii) user's interests are extracted from search history and related to given situations, and (iii) a case-based reasoning (CBR) approach is applied to select a suitable profile according to the current situation, in order to provide personalised search results.…”

Section: Time Awarenessmentioning

confidence: 99%

From IR to Mobile IR

Crestani

Mizzaro

Scagnetto

2017

Mobile Information Retrieval

View full text Add to dashboard Cite

https://adwords.googleblog.com/2015/05/building-for-next-moment. html * The use of the word "mainly" here indicates that the boundary between the two areas is becoming fuzzy, with some IR systems working in a more deterministic way and DB systems working in a more probabilistic or "uncertainty conscious" fashion. * The semantic meaning of different words is inferred using a deep learning technique named word2vec, https://code.google.com/p/word2vec/.† https://code.google.com/p/androguard/ * trec.nist.gov † research.nii.ac.jp/ntcir/index-en.html ‡ https://sites.google.com/site/treccontext/

show abstract

“…In [1,4,5] the authors propose a method which consists of building a dynamic situation and user profile based on time and user's experience. The user's preferences and interests in the user profile are weighted according to the situation (time, location) and user behavior.…”

Section: Managing the User's Situationmentioning

confidence: 99%

“…• Inspired by models of human reasoning developed by [7] in robotic, we propose to consider the user's situation in the bandit algorithm by using the case-based reasoning technique, which is not considered in [3,4,14].…”

Section: Managing the User's Situationmentioning

confidence: 99%

Hybrid-ε-greedy for Mobile Context-Aware Recommender System

Bouneffouf

Bouzeghoub

Gançarski

2012

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Abstract. The wide development of mobile applications provides a considerable amount of data of all types. In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Our work consists in applying machine learning techniques and reasoning process in order to adapt dynamically the MCRS to the evolution of the user's interest. To achieve this goal, we propose to combine bandit algorithm and case-based reasoning in order to define a contextual recommendation process based on different context dimensions (social, temporal and location). This paper describes our ongoing work on the implementation of a MCRS based on a hybrid-ε-greedy algorithm. It also presents preliminary results by comparing the hybrid-ε-greedy and the standard ε-greedy algorithm.Keywords: Machine learning, contextual bandit, personalization, recommender systems, exploration/exploitation dilemma. IntroductionMobile technologies have made access to a huge collection of information, anywhere and anytime. Thereby, information is customized according to users' needs and preferences. This brings big challenges for the Recommender System field. Indeed, technical features of mobile devices yield to navigation practices which are more difficult than the traditional navigation task. A considerable amount of research has been done in recommending relevant information for mobile users. Earlier techniques [8,10] are based solely on the computational behavior of the user to model his interests regardless of his surrounding environment (location, time, near people). The main limitation of such approaches is that they do not take into account the dynamicity of the user's context. This gives rise to another category of recommendation techniques that tackle this limitation by building situation-aware user profiles. However, these techniques have some problems, namely how to recommend information to the user in order to follow the evolution of his interest.In order to give Mobile Context-aware Recommender Systems (MCRS) the capability to provide the mobile user information matching his/her situation and adapted to the evolution of his/her interests, our contribution consists of mixing bandit algorithm (BA) and case-based reasoning (CBR) methods in order to tackle these two issues:

show abstract

Dynamically Personalizing Search Results for Mobile Users

Cited by 11 publications

References 20 publications

Inferring situation-based interests for mobile users

Inferring situation-based interests for mobile users

From IR to Mobile IR

Hybrid-ε-greedy for Mobile Context-Aware Recommender System

Contact Info

Product

Resources

About