Edgar Jembere

Publications

2019

Ikram, M. S., Pillay, A. ., & Jembere, E. . (2019). Using social networks to enhance a deep learning approach to solve the cold-start problem in recommender systems. In Forum for Artificial Intelligence Research (FAIR2019). Cape Town: CEUR. Retrieved from http://ceur-ws.org/Vol-2540/FAIR2019_paper_51.pdf

Abstract BibTex Entry BibTeX Download PDF

paper

The Cold-Start problem refers to the initial sparsity of data available to Recommender Systems that leads to poor recommendations to users. This research compares a Deep Learning Approach, a Deep Learning Approach that makes use of social information and Matrix Factorization. The social information was used to form communities of users. The intuition behind this approach is that users within a given community are likely to have similar interests. A community detection algorithm was used to group users. Thereafter a deep learning model was trained on each community. The comparative models were evaluated on the Yelp Round 9 Academic Dataset. The dataset was pruned to consist only of users with at least 1 social link. The evaluation metrics used were Mean Squared Error (MSE) and Mean Absolute Error (MAE). The evaluation was carried out using 5-fold cross-validation. The results showed that the use of social information improved on the results achieved from the Deep Learning Approach, and grouping users into communities was advantageous. However, the Deep Learning Approach that made use of social information did not outperform SVD++, a state of the art approach for recommender systems. However, the new approach shows promise for improving Deep Learning models.

@{243,
  author = {Muhammad Ikram and Anban Pillay and Edgar Jembere},
  title = {Using social networks to enhance a deep learning approach to solve the cold-start problem in recommender systems},
  abstract = {The Cold-Start problem refers to the initial sparsity of data available to Recommender Systems that leads to poor recommendations to users. This research compares a Deep Learning Approach, a Deep Learning Approach that makes use of social information and Matrix Factorization. The social information was used to form communities of users. The intuition behind this approach is that users within a given community are likely to have similar interests. A community detection algorithm was used to group users. Thereafter a deep learning model was trained on each community. The comparative models were evaluated on the Yelp Round 9 Academic Dataset. The dataset was pruned to consist only of users with at least 1 social link. The evaluation metrics used were Mean Squared Error (MSE) and Mean Absolute Error (MAE). The evaluation was carried out using 5-fold cross-validation. The results showed that the use of social information improved on the results achieved from the Deep Learning Approach, and grouping users into communities was advantageous. However, the Deep Learning Approach that made use of social information did not outperform SVD++, a state of the art approach for recommender systems. However, the new approach shows promise for improving Deep Learning models.},
  year = {2019},
  journal = {Forum for Artificial Intelligence Research (FAIR2019)},
  pages = {173-184},
  month = {4/12-6/12},
  publisher = {CEUR},
  address = {Cape Town},
  url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_51.pdf},
}

Jeewa, A. ., Pillay, A. ., & Jembere, E. . (2019). Directed curiosity-driven exploration in hard exploration, sparse reward environments. In Forum for Artificial Intelligence Research (FAIR). Cape Town: CEUR. Retrieved from http://ceur-ws.org/Vol-2540/FAIR2019_paper_42.pdf

Abstract BibTex Entry BibTeX Download PDF

Paper

Training agents in hard exploration, sparse reward environments is a diﬃcult task since the reward feedback is insuﬃcient for meaningful learning. In this work, we propose a new technique, called Directed Curiosity, that is a hybrid of Curiosity-Driven Exploration and distancebased reward shaping. The technique is evaluated in a custom navigation task where an agent tries to learn the shortest path to a distant target, in environments of varying diﬃculty. The technique is compared to agents trained with only a shaped reward signal, a curiosity signal as well as a sparse reward signal. It is shown that directed curiosity is the most successful in hard exploration environments, with the beneﬁts of the approach being highlighted in environments with numerous obstacles and decision points. The limitations of the shaped reward function are also discussed.

@{242,
  author = {Asad Jeewa and Anban Pillay and Edgar Jembere},
  title = {Directed curiosity-driven exploration in hard exploration, sparse reward environments},
  abstract = {Training agents in hard exploration, sparse reward environments is a diﬃcult task since the reward feedback is insuﬃcient for meaningful learning. In this work, we propose a new technique, called Directed Curiosity, that is a hybrid of Curiosity-Driven Exploration and distancebased reward shaping. The technique is evaluated in a custom navigation task where an agent tries to learn the shortest path to a distant target, in environments of varying diﬃculty. The technique is compared to agents trained with only a shaped reward signal, a curiosity signal as well as a sparse reward signal. It is shown that directed curiosity is the most successful in hard exploration environments, with the beneﬁts of the approach being highlighted in environments with numerous obstacles and decision points. The limitations of the shaped reward function are also discussed.},
  year = {2019},
  journal = {Forum for Artificial Intelligence Research (FAIR)},
  pages = {12 -24},
  month = {4/12-6/12},
  publisher = {CEUR},
  address = {Cape Town},
  url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_42.pdf},
}

2018

Jembere, E. ., Rawatlal, R. ., & Pillay, A. . (2018). Matrix Factorisation for Predicting Student Performance. In 2017 7th World Engineering Education Forum (WEEF). IEEE.

Abstract BibTex Entry BibTeX Download PDF

WeeF-Paper-2017-Final.pdf

Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.

@{194,
  author = {Edgar Jembere and Randhir Rawatlal and Anban Pillay},
  title = {Matrix Factorisation for Predicting Student Performance},
  abstract = {Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student’s mark for a module, given the student’s performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu-Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.},
  year = {2018},
  journal = {2017 7th World Engineering Education Forum (WEEF)},
  pages = {513-518},
  month = {13/11-16/11},
  publisher = {IEEE},
  isbn = {978-1-5386-1523-2},
}

Dzitiro, J. ., Jembere, E. ., & Pillay, A. . (2018). A DeepQA Based Real-Time Document Recommender System. In Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018. South Africa: SATNAC.

Abstract BibTex Entry BibTeX Download PDF

SATNAC 2018.pdf

Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high conﬁdence recommendations performs better than a classical content-based ﬁltering solution. The results obtained using the proposed methodology conﬁrmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high conﬁdence recommendations.

@{189,
  author = {Joshua Dzitiro and Edgar Jembere and Anban Pillay},
  title = {A DeepQA Based Real-Time Document Recommender System},
  abstract = {Recommending relevant documents to users in real- time as they compose their own documents differs from the traditional task of recommending products to users. Variation in the users’ interests as they work on their documents can undermine the effectiveness of classical recommender system techniques that depend heavily on off-line data. This necessitates the use of real-time data gathered as the user is composing a document to determine which documents the user will most likely be interested in. Classical methodologies for evaluating recommender systems are not appropriate for this problem. This paper proposed a methodology for evaluating real-time document recommender system solutions. The proposed method- ology was then used to show that a solution that anticipates a user’s interest and makes only high conﬁdence recommendations performs better than a classical content-based ﬁltering solution. The results obtained using the proposed methodology conﬁrmed that there is a need for a new breed of recommender systems algorithms for real-time document recommender systems that can anticipate the user’s interest and make only high conﬁdence recommendations.},
  year = {2018},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2018},
  pages = {304-309},
  month = {02/09-05/09},
  publisher = {SATNAC},
  address = {South Africa},
}