International Business Machines Corporation

Wipro Limited

Ria Chakraborty

Kartikeya Vats

Sujoy Kumar Roy Chowdhury

Tanveer Akhter Khan

Malathi Bellam Soundararajan

Khyati Baradia

Venkatakrishnan Rajaram

Narayanan Ramani Konnayar

Yogesh Narasimha

A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.

Deep cognitive constrained filtering for product recommendation

The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.

Method and system for automatically identifying issues in one or more tickets of an organization

Growing community of inventors

Kolkata, India

Ria Chakraborty