TY  - BOOK
AU  - Mohamed Reda Zohair Ali Bennawy
TI  - Real-Time Stream Processing For Recommendation Engine(s)
U1  - 610 
PY  - 2023///
KW  - Informatics-IFM
KW  - NULIB
KW  - Dissertation, Academic
N1  - Thesis (M.A.)—Nile University, Egypt, 2023; "Includes bibliographical references"; Contents:
List of Abbreviations.................................................................................................................. i
List of Tables............................................................................................................................. ii
List of Figures .......................................................................................................................... iii
Chapter 1: Introduction...............................................................................................................1
1.1 Motivation ........................................................................................................................1
1.2 Problem Statement ...........................................................................................................1
1.3 User Stories ......................................................................................................................2
1.4 Challenges........................................................................................................................3
1.5 Summary of Contributions...............................................................................................3
1.6 Thesis Outline...................................................................................................................4
Chapter 2: Literature Survey ......................................................................................................5
2.1 Introduction ......................................................................................................................5
2.2. Data Architecture ............................................................................................................6
2.3 Use Cases .........................................................................................................................9
2.4 Frameworks....................................................................................................................12
2.5 Comparison ....................................................................................................................22
2.6 Architecture ....................................................................................................................24
2.6.1 Benefits of modern stream processing architecture .................................................25
2.7 Session-based Recommender Systems...........................................................................26
2.8 Evaluation of any recommender system:........................................................................27
2.8.1 A/B Testing..............................................................................................................27
2.8.2 Rating Prediction Accuracy .....................................................................................28
2.8.3 Ranking Measures....................................................................................................29
2.8.4 Hit Rates Measures..................................................................................................30
2.8.5 Diversity Measures..................................................................................................31
2.9 Recommender Model Types...........................................................................................32
2.9.1 Content-Based Recommender System.....................................................................33
2.9.2 Collaborative Filtering Recommender System........................................................34
Chapter 3: Methodology...........................................................................................................42
3.1 Introduction ....................................................................................................................42
3.2 E-Tourism Datasets........................................................................................................42
3.3 Trivago Dataset Description...........................................................................................43
3.3.1 Overview..................................................................................................................43
3.3.2 Files Description ......................................................................................................46
3.3.3 Descriptive Analysis................................................................................................46
Chapter 4: Results and Discussions..........................................................................................52
4.1 Experiments....................................................................................................................52
4.2 Evaluation Metrics .........................................................................................................52
4.3 Experiments....................................................................................................................53
4.4 Benchmarks....................................................................................................................53
4.5 Proposed Solution...........................................................................................................56
Chapter 5: Conclusions and Future Work ................................................................................58
5.1 Conclusion......................................................................................................................58
5.2 Future directions.............................................................................................................59
References................................................................................................................................6
N2  - Abstract:
Event stream processing (ESP) is a data processing methodology which tackle online 
processing for a variety of events. Recently stream processing witnessed a huge 
interest in both academic research and corporate use cases. As a consequence, for the 
extremely huge data sources recently generated and diversely of usage. Data sources 
vary from websites logs, social media feeds, news articles, internal business 
transactions, IoT devices logs, ... etc. Academically, a lot of research papers discuss 
how to deal with enormous cloud of events in real-time with different data structures 
such as text, video, logs, transactions, … etc. Aside with, highlighting the weakness 
and strength points of the streaming platforms technologies. From corporate point of 
view, decision makers ask about how to best utilize those events with minimal delay 
in order to 1) uncover insights in real-time, 2) mine textual events, 3) recommend 
decisions. This requires a mix of machine learning, stream processing technologies 
and modern architecture to achieve best utilization with low latency. Unfortunately, 
each technology is typically optimized independently therefore, it is a challenge to 
combine all technologies together and have a scalable real-world application. 
Through the thesis, we shall discuss the state-of-the-art event stream processing 
technologies by summarizing definitions, data flow architectures, use cases, 
frameworks, and architecture best practices Also, we propose a recommendation 
engine architecture to perfectly cope with a real-life data stream in the E-Tourism 
domain.
The Association for Computing Machinery ACM recommendation systems challenge 
(ACM RecSys) [1] released an e-tourism dataset for the first time in 2019. Challenge 
shared hotel booking sessions from the trivago website asking to rank the hotel`s list 
for the users. The better ranking should achieve a high click-out rate. We introduce a
state of art architecture and a session-based recommender system on top of portal 
streaming data. Proposed solution take into consideration both recommendation 
engine accuracy and a low latency architecture. Compared to different benchmark 
publications on same dataset, proposed solution outperform in time with 10x faster 
using only 2% of computational power used. Paper shared the architecture and 
recommendation engine into details taking into consideration the ability to deploy the
model into real-life production environments.
ER  -