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Towards Effective Approaches for News Recommendation SystemAuthor: Vaibhav Kumar Date: 2018-07-30 Report no: IIIT/TH/2018/61 Advisor:Vasudeva Varma AbstractDeep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for news recommendation systems has received a relatively less amount of attention. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. With news stories coming from a variety of sources, it is crucial for news aggregators to present interesting articles to the user to maximize their engagement. This creates the need to have a recommendation system which accounts for both the content of the articles and the user preferences. Methods such as Collaborative Filtering, which are well known for general recommendations, are not suitable for news because of the short life span of articles and because of the large amount of articles published each day. Typically, it is desirable that a news recommendation system be able to discriminate between and select articles from a pool to recommend to a user as soon as they are published. Apart from this, such methods do not harness the information present in the sequence in which the articles are read by the user and hence are unable to account for the various interests of the user which may keep changing with time. Alternatively, the other class of models based on Content Filtering, can handle cold start problems but in the long run tend to suffer from the problem of over specialization. In this thesis, we address these issues in a step-by-step manner. We start off with a problem that is commonly associated with deep learning based methods i.e lack of sufficient data. In order to tackle this issue we come up with an item-based collaborative filtering approach which utilizes Markov Decision Process (MDP). We also come up with a novel semantic similarity measure which we incorporate as a reward for the MDP. This helps us gain insights about the various interests that users may have by only having prior knowledge of a few articles read by them in the past. We then move on to the exploration of various deep learning based methods to tackle our problem. We give importance to utilizing the content of the articles, and taking into account the historical reading data of a user. We attempt to solve the cold-start problem, and make effective recommendations for users who have had little to no interaction with items. We design the model to keep learning parameters based on implicit feedback from the users. A description of how we go about it is described as follows. We first come up with a user profiling based approach and use it in combination with a Deep Semantic Structured Model (DSSM). We then later expand the model and utilize a recurrent neural network with an attention mechanism. Such a mechanism helps us discriminate between the various interests of the user. We then come up with the Recurrent Attentive Recommendation Engine (RARE). RARE consistsof two components and utilizes the distributed representations of news articles. The first component is used to model the user’s sequential behaviour of news reading in order to understand her general interests i.e to get a summary of her interests. The second component utilizes an article level attention mechanism to understand her particular preferences. We feed the information obtained from both the components to a Siamese Network in order to make predictions which pertain to the user’s generic as well as specific interests. We carry out extensive experiments to establish the effectiveness of our methods. We also perform experiments to prove the efficacy of our model on solving the item cold-start cases as well as making effective recommendations for users who have had very little interaction with articles. Finally, we experiment with a novel 3D Convolutional Neural Network based model in an attempt to solve similar problems as the ones we tried to address earlier. Applying 3D convolution helped us identify both spatial (features of a particular article) as well as temporal information (features present in the sequence of articles read by the user) which are pertinent to a user’s interest. The initial results of this experimentation are also presented in this thesis. Full thesis: pdf Centre for Search and Information Extraction Lab |
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