January 16, 2019 – /,
Recommendation systems, in general, aim to reduce this burden of information overload by suggesting items of interest to a user. They imitate the human way of thinking by choosing as other like-minded people have chosen before when a decisive rst-hand knowledge is absent. Accordingly, they the recommendation to the user's interest when additional meta-information is available.
An increasing number of companies and e-publishers are utilizing recommender systems in order to maximize the user's satisfaction and thus increase their return investment (ROI). This is realized by leveraging the collected information about the user's interactions, preferences, and purchase history.
Traditional recommender systems, which aim to recommend documents, rely on statistical information only (collaborative filtering approaches) or on meta-information describing the user profile, the documents as well as their relations. They poorly consider the text semantics and mostly suffer from homophily by recommending documents which are similar to the documents already acquired by the user.
In this thesis, we want to design a query-based recommender system for the recommendation of textual documents. The frontend of this recommender is a Chatbot that interacts with the user and gets his preferences. The backend relies on Latent Semantic Model (CLSM) to implicitly measure the semantic similarity between the user’s interest and available documents. Taking into account the user’s previous activities, knowledge and current interests. The system represents the user’s history as a query of keywords. A graph-database will be used to project the semantic relations between the documents data set.
In this work, we aim to develop/adapt machine learning methods (mainly Deep Learning) to improve the state-of-the-art in <g class="gr_ gr_86 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del multiReplace" id="86" data-gr-id="86">contnet</g>-based recommender systems. This includes:
The written report must contain an introduction to the topic and provide an overview of related work. Furthermore, the designed and implemented methods must be described and discussed.
• Good programming skills in mind. A high-level language mainly python
• Helpful: Previous experience in Natural Language Processing and Machine Learning
Beginning and <g class="gr_ gr_136 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del multiReplace" id="136" data-gr-id="136">Dduration</g>
<g class="gr_ gr_136 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="136" data-gr-id="136">Immediately</g>, duration 3-6 months (depending on the course)
Keywords: Recommeder System, Word Embeddings,Graph-database, Semantic-based Recommender, Ontology-based Recommender
Research Area(s): Knowledge & Educational Technologies