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Kauppakorkeakoulun julkaisuportaali
Aalto-yliopiston kauppakorkeakoulun gradujen tiedot nyt Aaltodocissa: Aaltodoc-julkaisuarkisto
Kauppakorkeakoulu | Tieto- ja palvelutalouden laitos | Tietojärjestelmätiede | 2014
Tutkielman numero: 13707
Recommendation systems - technology and business aspects
Tekijä: Stenberg, Markus
Otsikko: Recommendation systems - technology and business aspects
Vuosi: 2014  Kieli: eng
Laitos: Tieto- ja palvelutalouden laitos
Aine: Tietojärjestelmätiede
Asiasanat: tietojärjestelmät; information systems; liiketalous; business economics; mallit; models; tietämyksenhallinta; knowledge management
Sivumäärä: 73
Avainsanat: big data; recommendation; recommendation system; prototype; business model
Tiivistelmä:
This thesis describes what recommendation systems are, what they are used for, how to build one, and what the potential business models around them are.

The work starts with a literature review of recommendation system usage, recommendation system algorithms, and how to evaluate them. As empirical part of the thesis, implementation effort of a prototype recommendation system is described. The prototype is then evaluated in terms of scalability and quality of recommendations.

Business part of the thesis discusses business models that are based on focusing around different parts of a typical e-commerce process which employs recommendation systems. The models are examined in two dimensions: customer segment type (business or consumer), and for company of what size they are applicable for.

The main conclusion of this thesis is that recommendation systems seem an important part of business. They also seem not very hard to implement, although selling one seems challenging based on failure to sell the produced prototype. It seems puzzling that there does not seem to be much business or even academic literature along the lines of business models noted within the thesis.

In technical implementation, there are obviously some pitfalls, and as the prototype never reached product quality, they may be perhaps under-emphasized. Some interesting work was produced on how to actually design and implement a highly scalable hybrid recommendation algorithm, with linear computational complexity scaling based on number of users, items and rankings to be processed.
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