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Knowledge Gain Driven Recommender Systems in e-LearningAuthor: Akhil Batra Date: 2022-03-10 Report no: IIIT/TH/2022/17 Advisor:Vikram Pudi AbstractThere is an explosion of information in the digital world. In a crowded digital world, recommender systems are a prerequisite for any digital media platform that serves content to users of varied interests. Platforms like Netflix, Amazon, Youtube have leveraged and contributed to the extensive work in recommender systems and are continuously improving the status quo. In the last decade, another kind of content-based platform has emerged – the EdTech kind. Though having the same need, to serve relevant and engaging content to its users, this domain however has very distinct nuances that make the traditional recommendation systems and the theory, metrics and impact factors around it misfit for its use case. For example, it makes sense for youtube to recommend a jingle that a user keeps listening to, it makes sense for netflix to recommend an all time favourite episode from a sitcom. Numerous experiments have shown that this infact helps to boost the users engagement on the platform. This approach does not work for EdTech where the intention is to have the user consume optimal content for knowledge gain. Repeat recommendations will in fact fatigue the user and drive him/ her off the platform. Another key nuance that we make a central theme in the thesis is the concept of ‘knowledge gain’ and posit that recommender systems for eLearning have to optimise this as a parameter. This is something that is unique to eLearning only and needs a careful thought. Overspecialization in the recommender systems is a big problem for edtech as it inhibits the users learning curve and ultimately reflects poorly on the edtech platforms catalogue and user journey. Thus, a healthy amount of knowledge gain injected by appropriate recommendations goes a long way in the eLearning domain. The limitations of the current ecosystem propelled us into an inquiry on the exact nature of these gaps. Building on, we propose a content based recommendation algorithm - ‘KNN-RkNN’. Inspired by the theory of active learning, our ‘KNN-RkNN’ (K Nearest Neighbour - Reverse K nearest neighbour) algorithm is optimised for information gain that’s learnt from the users history. The data was sourced from a popular EdTech platform, Unacademy. The effectiveness of this algorithm was measured against relevance, diversity and NDCG (Normalised discounted cumulative gain). This algorithm was tuned in perspective of optimising for knowledge gain. We show that taking informativeness as a feature, independent of the diversity and relevance, can lead to an unbiased exploration of good content in a large content repository. This thinking is a new line of thought in edtech and paves way for further research on development of recommendation algorithms primed on informativeness as a feature.We also research on building an enhanced sequential collaborative filtering technique that learns from the users watch history and recommends a learning path/playlist. This recommendation engine consists of a seq-2-seq model with multiple LSTM layers with attention mechanism. The results of this experiment are encouraging. An accuracy above 90% is achieved when the number of classes to recommend are reduced. Different experiments with similar architectures yielded different results which have been documented in this study. The chief contribution of this research is to qualitatively establish how knowledge gain when considered as a objective for eLearning is a nuance that was missing in eLearning thus far. We also quantitatively establish this via multiple case studies. Byproduct of these studies is to demonstrate how knowledge gain can be incorporated in both content based and collaborative filtering techniques. A more subtle contribution of our work is its lab to market nature. The sequential collaborative learning algorithm explained in the thesis is industry ready both for performance and scale. This research hopes to serve as a base for future studies to tune knowledge gain not just in content but also in other eLearning aspects like polls, tests, discussions etc. Full thesis: pdf Centre for Data Engineering |
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