Gerard Salton's scientific contributions

Citations

... To better reflect users' preferences and to interpret the meaning of the items, in this paper, we explored the effectiveness of utilizing semantic knowledge (meaningful relationships between items) learnt through the use of bottom-up models based on distributional hypothesis such as Doc2vec [43] and TF-IDF [53] methods which consider the con-text of item usage, e.g., their co-occurrence in the purchase sequences to learn the semantic relationships between products (e.g., products co-purchased and co-reviewed along with computing semantic similarities based on their textual features). This semantic knowledge can then be integrated into the Markov process for personalized sequential recommendation process by (i) learning semantic associations between items (ii) creating item transition probability matrix by first extracting the sequential co-occurrences of product pairs, normalizing it and then (iii) fusing the semantic knowledge into the transition probability matrix and using it with users' preferences (personalized vector) to generate semantically similar, sequential next item recommendations. ...