A novel recommendation approach based on chronological cohesive units in content consuming logs

Abstract We propose a novel recommendation approach based on chronological cohesive units (CCUs) of content consuming logs. Chronological cohesive units are defined as sub-sequences of logs in which items are highly related to each other. We first generate rules for splitting consuming logs into CCUs. We select features which are effective for splitting of consuming logs and combine them into a binary decision tree to generate splitting rules with genetic programming. With the rules, we split content consuming logs into CCUs, and identify strongly associated items in the CCUs. Next items are recommended with an association rule-based approach. The proposed method is evaluated using two-real datasets: web page navigation logs and movie consuming logs. The experiments confirm that the proposed approach is superior to the existing methods in various aspects such as hit ratio, click-soon ratio, sparsity, diversity and serendipity.

[1]  Donghoon Lee,et al.  A music recommendation system based on personal preference analysis , 2008, 2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT).

[2]  Ruchit V. Mehta,et al.  Session Based Music Recommendation using Singular Value Decomposition (SVD) , 2012 .

[3]  Alejandro Bellogín,et al.  Time-Aware Evaluation of Methods for Identifying Active Household Members in Recommender Systems , 2013, CAEPIA.

[4]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[5]  Hassan Silkan,et al.  Prediction of customers' needs: An approach based on similarity search in transactions databases , 2016, 2016 International Conference on Information Technology for Organizations Development (IT4OD).

[6]  Robert M. Bell,et al.  The BellKor 2008 Solution to the Netflix Prize , 2008 .

[7]  Tranos Zuva,et al.  Diversity and Serendipity in Recommender Systems , 2017, BDIOT2017.

[8]  Jing Peng,et al.  Learning latent factor from review text and rating for recommendation , 2015, 2015 7th International Conference on Modelling, Identification and Control (ICMIC).

[9]  Ricardo Dias,et al.  Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context , 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.

[10]  Xu Chi,et al.  Profit estimation error analysis in recommender systems based on association rules , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[11]  Enrique Herrera-Viedma,et al.  REFORE: A recommender system for researchers based on bibliometrics , 2015, Appl. Soft Comput..

[12]  Diyi Yang,et al.  Serendipitous Personalized Ranking for Top-N Recommendation , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

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

[14]  Anamika Rajput,et al.  User Rating and Synonyms Based Modified Ranking Technique for Recommender Systems , 2015, 2015 International Conference on Computational Intelligence and Communication Networks (CICN).

[15]  Pasquale Lops,et al.  An investigation on the serendipity problem in recommender systems , 2015, Inf. Process. Manag..

[16]  Bong-Jin Yum,et al.  Recommender system based on click stream data using association rule mining , 2011, Expert Syst. Appl..

[17]  Yeounoh Chung,et al.  Improved Neighborhood Search for Collaborative Filtering , 2018, Int. J. Fuzzy Log. Intell. Syst..

[18]  Iván Cantador,et al.  Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols , 2013, User Modeling and User-Adapted Interaction.

[19]  Qing Liao,et al.  Weight Based KNN Recommender System , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[20]  Kyogu Lee,et al.  Recommending Music Based on Probabilistic Latent Semantic Analysis on Korean Radio Episodes , 2013, 2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[21]  Ravi Kumar,et al.  Modeling User Consumption Sequences , 2016, WWW.

[22]  Le Song,et al.  Time-Sensitive Recommendation From Recurrent User Activities , 2015, NIPS.

[23]  Yang Song,et al.  Task Trail: An Effective Segmentation of User Search Behavior , 2014, IEEE Transactions on Knowledge and Data Engineering.

[24]  Michael Bieber,et al.  A clickstream-based collaborative filtering personalization model: towards a better performance , 2004, WIDM '04.

[25]  Jee-Hyong Lee,et al.  An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming , 2012, IEICE Trans. Commun..

[26]  Shing-Tai Pan,et al.  Improving efficiency of recommender systems , 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

[27]  Ruisheng Zhang,et al.  Collaborative Filtering for Recommender Systems , 2014 .

[28]  Jei-Zheng Wu,et al.  A recommender system based on car pairwise comparisons on a mobile application using association rules , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[29]  Li Li,et al.  Recommender System Based on Random Walk with Topic Model , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[30]  Anisa W. Ragalo,et al.  A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming , 2012, 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC).

[31]  Derek Bridge,et al.  Diversity, Serendipity, Novelty, and Coverage , 2016, ACM Trans. Interact. Intell. Syst..

[32]  Zhifang Liao,et al.  Content-Based Filtering Recommendation Algorithm Using HMM , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[33]  A. Vineela,et al.  An agglomerative hierarchical clustering for Hybrid Recommender Systems , 2015, 2015 Conference on Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG).

[34]  Qian Zhao,et al.  Investigating serendipity in recommender systems based on real user feedback , 2018, SAC.

[35]  Sang-goo Lee,et al.  Session-Based Collaborative Filtering for Predicting the Next Song , 2011, 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering.

[36]  Mahdi Jalili,et al.  A Time-Aware Recommender System Based on Dependency Network of Items , 2015, Comput. J..

[37]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.

[38]  Anand Shanker Tewari,et al.  Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[39]  Vineet Padmanabhan,et al.  Group Recommender Systems: Combining User-User and Item-Item Collaborative Filtering Techniques , 2015, 2015 International Conference on Information Technology (ICIT).

[40]  Bing Wu,et al.  A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications , 2016, IEEE Access.

[41]  Pei-Chann Chang,et al.  Application of artificial immune systems combines collaborative filtering in movie recommendation system , 2014, Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[42]  Cynthia Rudin,et al.  Sequential event prediction , 2013, Machine Learning.