Master Project. Various Aspects of Recommender Systems. Prof. Dr. Georg Lausen Dr. Michael Färber Anas Alzoghbi Victor Anthony Arrascue Ayala

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1 Master Project Various Aspects of Recommender Systems May 2nd, 2017 Master project SS17 Albert-Ludwigs-Universität Freiburg Prof. Dr. Georg Lausen Dr. Michael Färber Anas Alzoghbi Victor Anthony Arrascue Ayala

2 Agenda Organization Recommender Systems Topics - Finding complementary products (Anthony) - Cross-domain recommendations (Anthony) - Scientific Paper recommendation (Anas) - Recommending new Wikipedia articles (Michael) - Recommending references for (scientific) texts (Michael) 2

3 Requirements Study regulations (Studienordnung) - 16 ECTS 480 hours Master project - Team size: 1-3 students - Project report: ~10-12 pages per student - Short presentations: 2-3 (individual as needed) - Final presentation: 25 min Some preconditions - Recommended lecture Data Analysis and Query Language or similar 3

4 General goals Collective work on a project Gain experience in research and development method Improve individual programming skills Incorporate in new topics (Semantic Web, Recommender systems, ) Learn about problems of larger projects 4

5 Assessment Workload of every student must be clearly distinguishable Some Criteria - Methodology - The scope and difficulty of the work / implementation - Individual contribution - Team performance: a successful project has a positive effect - Role and participation in the team (coordination, etc.) - Quality of code (formatting, documentation, testing) - Individual report (project report) - Presentations (especially the final presentation) 5

6 Organization Meetings - Building 51 SR Website - Apply via HISinOne SVN repository Various Aspects of Recommender Systems SS17 6

7 Master projects 1. Finding complementary products (Anthony) 2. Cross-domain recommendations (Anthony) 3. Scientific Paper recommendation (Anas) 4. Recommending new Wikipedia articles (Michael) 5. Recommending references for (scientific) texts (Michael) Various Aspects of Recommender Systems SS17 7

8 Finding complementary products - 1 st project Products that are sold separately but that are used together, each creating a demand for the other Click 8

9 CP Traditional Approaches Data Mining (Association Rules) - Require transactions Limitations - Cold start for new items - Unpopular products - No explanations 10

10 CP Problem Predict if complementary relationship holds No transactions Using Semantic Web technologies - Linked Open Data (DBpedia): knowledge graph Based on product s meta-data - Publicly available 11

11 CP Solution scheme Learning to Identify Complementary Products from Dbpedia. Victor Anthony Arrascue Ayala, Trong-Nghia Cheng, Anas Alzoghbi, Georg Lausen Evaluation using Amazon s data 12

12 Goal: improving the scheme 1. Reproduce pipeline 2. Add new features - Observable graph-features - Meta-data: e.g. price 3. Extend evaluation - Other categories (Books, Movies and TV, etc.) - Ranking vs. classification 13

13 Compulsory task 1. Read the paper 2. Extract products attributes - Smallest category - Using NER tool (Alchemy / Spotlight) 3. Create knowledge graph - Crawl links between attributes from DBpedia 4. Data analysis - Products coverage - Interconnection s quality - Etc Various Aspects of Recommender Systems SS17 14

14 Submission of compulsory task Pre-requisite to participation Report - Introduction - Problem statement (1 page) - Solution proposal (1 page) - Data analysis (2 pages) - Related work (1 pages) 1 team, max. 3 students Deadline: , 12: Various Aspects of Recommender Systems SS17 15

15 Cross-domain recommendations - 2 nd project The research on cross-domain recommendation generally aims to exploit knowledge from a source domain D S to perform or improve recommendations in a target domain D T [RS Handbook]??? 16

16 CDRS Problem For each user - Given a set of likes for items in D S - Predict items in D T Using Semantic Web technologies - Linked Open Data (DBpedia): knowledge graph - Items are interconnected 17

17 CP Solution scheme (not assessed) Learning to Identify Complementary Products from Dbpedia. Victor Anthony Arrascue Ayala, Trong-Nghia Cheng, Anas Alzoghbi, Georg Lausen Evaluation using Facebook s data (likes) 18

18 CP Solution scheme (not assessed) Learning to Identify Complementary Products from Dbpedia. Victor Anthony Arrascue Ayala, Trong-Nghia Cheng, Anas Alzoghbi, Georg Lausen Evaluation using Facebook s data (likes) Liked? 19

19 Goal: try the scheme 1. Reproduce pipeline 2. Implement a recommender on top - Predict if a user would like the item - Predict top-k recommendations - *Optional: Integrate into RecRD4J 3. Evaluate the recommender - Use standard metrics: Precision, Recall 20

20 Compulsory task 1. Read the paper 2. Build infrastructure - Large dataset (approx. 15 GB) 3. Data analysis - For each domain (books, movies, music) - Interconnection s quality - Long-tail - Sparsity - Etc Various Aspects of Recommender Systems SS17 21

21 Submission of compulsory task Pre-requisite to participation Report - Introduction - Problem statement (1 page) - Solution proposal (1 page) - Data analysis (2 pages) - Related work (1 pages) 1 team, max. 3 students Deadline: , 12: Various Aspects of Recommender Systems SS17 22

22 Scientific Paper recommendation- 3 rd project Recommend Scientific papers to users Content-Based, Collaborative filtering and Hybrid Papers features (meta-data) - Textual features: Title, Abstract, Keyword list - Non-textual features: Publication year, Authors, Venue, Publisher, 23

23 Scientific Paper recommendation- 3 rd project Textual paper representation Term Extraction k 1 k i k i+1 k n 1 1 tf-idf i+1 tf-idf n Paper Paper Vector Various Aspects of Recommender Systems SS17 24

24 Scientific Paper recommendation- 3 rd project Rating Matrix 25

25 HyPRec Master Project WS 2016 Scientific papers recommender Probabilistic Topic Modeling (LDA) Matrix factorization (ALS Algorithm) Python GitHub 26

26 HyPRec - Architecture Evaluator Metrics Calculator Train-Test splitter MRR, NDCG, Recall User-Based K-Fold Split Recommender Papers Model Content-Based Filtering Collaborative Filtering Hybrid Item-based CBF Matrix Factorization CF Weighted (Linear Combination) Citeulike Dataset (csv files) Data Parser Mysql DB Textual representation Latent topics Features Tf-IDF LDA Publication year, authors, publisher,... 27

27 Regulations One team max 3 students Weekly meetings Programming language: Python Various Aspects of Recommender Systems SS17 28

28 Regulations Compulsory task (Deadline: , Pre-requisite to participation) - Get familiar with HyPRec - Implement a simple Recommender (User-based CF) - Submit evaluation results (small presentation) Starting Report (Submission: ) - Problem statement (1 page) - Solution proposal (1 page) Various Aspects of Recommender Systems SS17 29

29 New Wikipedia Article Recommendation - 4 th project 30

30 Motivation: Writing New Wikipedia Articles Dan Fredinburg Michael Slager What to write about? Adult Beginners Oleg Kalashnikov LG G4 What to do? 1. Use list of requested articles 2. Read news or consume other media. Automatically recommend relevant novel Wikipedia articles based on news stream. 31

31 Distinguish between notable and not-notable entities Various Aspects of Recommender Systems SS17

32 Approach: Use diff between Wikipedia dumps 33

33 Existing Approach for Recommending New Wikipedia Articles see Färber et al.: On Emerging Entity Detection, EKAW

34 Task Build a live system for Wikipedia article recommendation. 35

35 Task Improve the system via - Better selection of news sources - Distributed processing of news articles (especially text annotation) - Considering also very recently added Wikipedia pages - Find and implement better features / adapt existing features - Improve binary classification, e.g., by using a Recurrent Neural Network. - Using word embeddings for better representation of candidates in news articles. - Using other Knowledge Graphs, e.g., Wikidata or CrunchBase. 36

36 Compulsory task 1. Read related work (esp., On Emerging Entity Detection, EKAW 2016). 2. Extract Wikipedia articles which were inserted between two Wikipedia dumps (given the Wikipedia indices). 3. Annotate news articles (from between the Wikipedia versions) via an entity linking tool and extract noun phrases. 4. Calculate statistics about annotations. 5. Correlate new Wikipedia articles and their mentions with metainformation of news articles (e.g., which sources are suitable for predicting new Wikipedia articles). 37

37 Submission of compulsory task 1 team, max. 2 students Report, Deadline: , 12:00, Pre-requisite to participation - Introduction (1 page) - Data analysis (2 pages) - Related work (1 page) Project proposal ( ) - Additional sections: Problem statement (1 page), proposed approach/improvements of the system (2 pages), proposed evaluation (1 page) 38

38 Citation Recommendation - 5 th project Idea: Enrich (scientific) text with citation markers (e.g, [1] ) and references. 39

39 Approach 1. Create model: - Extract citations with context from publication corpus. - Develop & implement features for ranking publications. 2. Apply model: - Extract citation contexts from input text. - Determine which publications to cite in which context. - Add citations to text. 40

40 Useful Data Sets Scholarly - 101k papers in computer science domain, PDF+metadata arxiv.org - Over 1M papers (PDF+metadata) - Different fields: Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics CiteSeerX - Database with publications and citations - Ca. 7M papers DBLP, Microsoft Academic Graph, 41

41 Compulsory task Read related work Analyze and compare existing data sets for citation recommendation, including - citation context extraction - publication meta-data retrieval - citation graph creation - incorporating external data sets (e.g., DBLP, PageRank, ) 42

42 Submission of compulsory task 1 team, max. 3 students Report, Deadline: , 12:00, Pre-requisite for participation - Introduction (1 page) - Analysis & comparison of data sets and tools (2 pages) - Related work (for task in general) (2 pages) Project proposal ( ) - Additional sections: Problem statement (1 page), proposed approach (2 pages), proposed evaluation (1 page) 43

43 Thank you! Any questions? 44