G5 Artikkeliväitöskirja
Utilization of efficient features, vectors and machine learning for ranking techniques (2019)

Pandey, G. (2019). Utilization of efficient features, vectors and machine learning for ranking techniques [Doctoral dissertation]. Jyväskylän yliopisto. JYU dissertations, 100. http://urn.fi/URN:ISBN:978-951-39-7806-8

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Pandey, Gaurav

eISBN: 978-951-39-7806-8

Lehti tai sarja: JYU dissertations

eISSN: 2489-9003

Julkaisuvuosi: 2019

Sarjan numero: 100

Kirjan kokonaissivumäärä: 1 verkkoaineisto (63 sivua, 66 sivua useina numerointijaksoina) :

Kustantaja: Jyväskylän yliopisto

Kustannuspaikka: Jyväskylä

Julkaisumaa: Suomi

Julkaisun kieli: englanti

Pysyvä verkko-osoite: http://urn.fi/URN:ISBN:978-951-39-7806-8

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Tiivistelmä

Document ranking systems and recommender systems are two of the most used applications on the internet. Document ranking systems search for documents in response to a query given by the user. On the other hand, recommender systems suggest items to the users on the basis of their previously expressed preferences. Both document ranking systems and recommender systems make use of ranking techniques, since they typically present their results in the form of a ranked list. The order of the results is important because the users expect the most useful results at the top of these ranked lists. Improvements in algorithms used by document ranking systems and recommender systems, including the utilization of advanced machine learning techniques, lead to the generation of improved rankings. Moreover, advanced document ranking systems often use features collected from the documents to generate rankings. Similarly, vectors generated for the users as well as items are utilized by the recommender systems. Therefore, generation of features and vectors of good quality is instrumental for ranking techniques. This dissertation makes the following contributions to explore the improvements in ranking techniques using efficient features, vectors and machine learning: a) Creation of a feature extraction algorithm for learning to rank tasks in document ranking, b) Creation of pairwise preference vectors of ratings on items by using neural embeddings that can be utilized in machine learning tasks including recommender systems, c) Utilization of deep neural networks and transfer learning for serendipitous recommendations, d) Recommendations using ranking probabilities and non-negative matrix factorization and e) Application of neural embeddings to search for cities and tours, taking user’s travel interests into account.

YSO-asiasanat: suosittelujärjestelmät; tiedonhakujärjestelmät; algoritmit; koneoppiminen; neuroverkot

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2019