Profile reliability to improve recommendation in social-learning context

Purpose - Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.Design/methodology/approach - This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings - The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications - Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value - This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

[1]  Bo Zhao,et al.  Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation , 2014, SIGMOD Conference.

[2]  Shuaiqiang Wang,et al.  A survey of serendipity in recommender systems , 2016, Knowl. Based Syst..

[3]  Corinne Amel Zayani,et al.  A novel architecture for learner's profiles interoperability , 2015, 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS).

[4]  María de Lourdes Martínez-Villaseñor,et al.  Enriquecimiento del perfil del estudiante mediante la interoperabilidad de modelos de usuario ubicuos , 2014, Computación y Sistemas.

[5]  Wael Hassan Gomaa,et al.  A Survey of Text Similarity Approaches , 2013 .

[6]  María de Lourdes Martínez-Villaseñor,et al.  Enrichment of Learner Profile with Ubiquitous User Model Interoperability , 2014 .

[7]  Divesh Srivastava,et al.  Characterizing and selecting fresh data sources , 2014, SIGMOD Conference.

[8]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[9]  Li Bi,et al.  A Survey of Recommender System , 2014 .

[10]  Florence Sèdes,et al.  A Topic-Based Hidden Markov Model for Real-Time Spam Tweets Filtering , 2017, KES.

[11]  Fenglong Ma,et al.  Influence-Aware Truth Discovery , 2016, CIKM.

[12]  Hong Yu,et al.  Tag recommendation method in folksonomy based on user tagging status , 2017, Journal of Intelligent Information Systems.

[13]  Wei He,et al.  A social tag clustering method based on common co-occurrence group similarity , 2016, Frontiers of Information Technology & Electronic Engineering.

[14]  Florence Sèdes,et al.  Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information , 2017, Data Knowl. Eng..

[15]  María de Lourdes Martínez-Villaseñor,et al.  Enrichment of Learner Profile with Ubiquitous User Model Interoperability , 2014 .

[16]  Vincent P. Wade,et al.  The FUSE domain-aware approach to user model interoperability: A comparative study , 2013, 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI).

[17]  C. Ravindranath Chowdary,et al.  ReLiC: Entity Profiling by using Random Forest and Trustworthiness of a Source - Technical Report , 2017, ArXiv.

[18]  Xiaoxin Yin,et al.  Semi-supervised truth discovery , 2011, WWW.

[19]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[20]  Alon Y. Halevy,et al.  Data integration with dependent sources , 2011, EDBT/ICDT '11.

[21]  Mong-Li Lee,et al.  Profiling Entities over Time in the Presence of Unreliable Sources , 2017, IEEE Transactions on Knowledge and Data Engineering.

[22]  Chunling Du,et al.  A survey on recommender systems , 2016, 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE).

[23]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[24]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[25]  Christopher Ré,et al.  SLiMFast: Guaranteed Results for Data Fusion and Source Reliability , 2015, SIGMOD Conference.

[26]  Robin Burke,et al.  Personalization in Folksonomies Based on Tag Clustering , 2008 .

[27]  Chao Huang,et al.  Topic-Aware Social Sensing with Arbitrary Source Dependency Graphs , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[28]  Florence Sèdes,et al.  Towards a User-aware Enrichment of Multimedia Metadata , 2010 .

[29]  Divesh Srivastava,et al.  Data Fusion: Resolving Conflicts from Multiple Sources , 2013, WAIM.

[30]  Taylor Cassidy,et al.  The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multi-dimensional Truth-Finding , 2014, COLING.

[31]  Alexander Felfernig,et al.  Basic Approaches in Recommendation Systems , 2014, Recommendation Systems in Software Engineering.

[32]  Florence Sèdes,et al.  Social collaborative service recommendation approach based on user's trust and domain-specific expertise , 2018, Future Gener. Comput. Syst..

[33]  Dan Roth,et al.  Knowing What to Believe (when you already know something) , 2010, COLING.

[34]  Kim-Kwang Raymond Choo,et al.  User profiling in intrusion detection: A review , 2016, J. Netw. Comput. Appl..

[35]  Zongda Wu,et al.  KIPTC: a kernel information propagation tag clustering algorithm , 2013, Journal of Intelligent Information Systems.

[36]  Florence Sèdes,et al.  Dynamic enrichment of social users' interests , 2014, 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS).

[37]  Wilfred Ng,et al.  Truth Discovery in Data Streams: A Single-Pass Probabilistic Approach , 2014, CIKM.

[38]  Edwin Simpson,et al.  Clustering Tags in Enterprise and Web Folksonomies , 2021, ICWSM.

[39]  Chao Huang,et al.  Towards social-aware interesting place finding in social sensing applications , 2017, Knowl. Based Syst..

[40]  Ahmad Abdollahzadeh Barforoush,et al.  A New Classification Framework to Evaluate the Entity Profiling on the Web , 2017, ACM Comput. Surv..