<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as <italic>space filling centered Recommender</italic> (<italic>SRec</italic>), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general <italic>SRec</italic> prototype is instantiated by us employing <italic>convolutional neural network</italic> (<italic>CNN</italic>) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that <italic>SRec</italic> performs better than traditional sequential recommendation approaches.</p>
</abstract>

Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.

Session centered Recommendation Utilizing Future Contexts in Social Media

Department of Computer Science & Engineering

University Institute of Engineering and Technology, Kurukshetra University

Department of Mathematics and Informatics

Analele ştiinţifice ale Universităţii "Ovidius" Constanţa. Seria Matematică

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Session centered Recommendation Utilizing Future Contexts in Social Media"}

Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a...

Session centered Recommendation Utilizing Future Contexts in Social Media

Data publikacji: 23 lis 2021

Zakres stron: 91 - 104

Otrzymano: 14 mar 2021

Przyjęty: 14 maj 2021

DOI: https://doi.org/10.2478/auom-2021-0036

Słowa kluczowe
Sequential Recommendation, Space-filling, seq2seq, Encoder and Decoder

© 2021 Sanjeev Dhawan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Session centered Recommendation Utilizing Future Contexts in Social Media

Data publikacji: 23 lis 2021

Zakres stron: 91 - 104

Otrzymano: 14 mar 2021

Przyjęty: 14 maj 2021

DOI: https://doi.org/10.2478/auom-2021-0036

Słowa kluczoweSequential Recommendation, Space-filling, seq2seq, Encoder and Decoder

© 2021 Sanjeev Dhawan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Sequential Recommendation, Space-filling, seq2seq, Encoder and Decoder