000			09227nam a22002537a 4500
008			201210b2023 a|||f bm|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aMohamed Reda Zohair Ali Bennawy _92624
245	1		_aReal-Time Stream Processing For Recommendation Engine(s)/ _cMohamed Reda Zohair Ali Bennawy
260			_c2023
300			_a75 p. _bill. _c21 cm.
500			_3Supervisor: Walid Al-Atabany
502			_aThesis (M.A.)—Nile University, Egypt, 2023 .
504			_a"Includes bibliographical references"
505	0		_aContents: 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
520	3		_aAbstract: 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.
546			_aText in English, abstracts in English and Arabic
650		4	_aInformatics-IFM _9266
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics-IFM _9266
942			_2ddc _cTH
999			_c9982 _d9982