A chat application that provides real-time recommendations when user discusses about movies, restaurants, products, travel etc. on chat. The application works on user click tracking and explicit user feedback to learn preferences and personalize future recommendations and improve classification accuracy. https://github.com/rishirdua/smart-chat

1. Smart Chat
Rishi Dua
Harvineet Singh
Real-time chat-based personalized recommendation system
Rishi Dua (IIT Delhi) Twitter Recommendation 28 May 2014 1 / 15

2. Smart Chat
Motivation
Combining Chat and Information
Limited features of traditional chat systems
No recommendations based on
User interests and needs
Schedule and planning
No application of NLP techniques for unstructured text (chat)
E-commerce websites: Amazon, Ebay etc.
Based on viewed and purchased products
More products suggested based on previous choice
Personalisation with Privacy
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3. Smart Chat
Problem Statement
Developing a chat application providing real time suggestions
Movies: Show timings, recent movies
Food: Restaurant reviews
Products: Reviews, shopping links
Travel to city: Train/
ight booking, Hotels, weather etc.
Plans/Meetings: Calendar and personal schedule
And so on..choice
Recommendations improved with user interaction
Learns preferences and interests
Recommendations personalised with user feedback
Tracking user clicks to improve classi

4. cation accuracy
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5. Smart Chat
Existing Work
Applications developed
GaChat: Returns information from Wikipedia relevant to users chat
SemChat: Extracting Personal Information from Chat Conversations
Current research
Topic detection and extraction in chat
Information extraction from chat corpus study
Recommendations improved with user interaction
Trained on

6. xed given corpus
None of the research techniques give real-time recommendations
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7. Smart Chat
Approach
Live Recommendation System
User sentences categorised into prede

8. ned categories: food, cars,
movies, travel etc.
Using NLP,

9. gure out the topic of conversation
Find relevant websites
Using open source search APIs to

10. nd recommendations from given
websites
Users feedback used to improve future recommendations
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11. Smart Chat
User Interface
Figure: Screenshot of the User Interface
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12. Smart Chat
Deciding when to give suggestion
Figure: Recommendation is shown when 3 consecutive sentences are from same
topic
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13. Smart Chat
Feedback from user to improve prediction
Figure: Screenshot of the User Interface
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14. Smart Chat
Tracking user preference
Figure: Ranking of recommendation gets updated when user clicks on a
recommendation
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15. Smart Chat
Privacy
Chat data saved anonymously in corpus
Training on chat session instead of users chat history
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16. Smart Chat
Algorithm - Classi

17. cation
1 Pre-processing: Stop words removal, Stemming using WorldNet
lemmatizer
2 Feature extraction: TF-IDF of word count vectors
3 Feature selection: Select k-best features using Chi-square feature
selection
4 Classi

18. cation: Multinomial NB, Bernoulli, LinearSVC, Perceptron
using scikit-learn library
5 Cross-validation: f-1 score to select best classi

19. er
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20. Smart Chat
Algorithm - Recommendations
1 Figure out the topic of conversation from learnt model
2 Mark chat as discussion if 3 consecutive sentences are from same
topic
3 If chat is a discussion
1 Search for recommendations from given websites using open source
search APIs
2 Track users clicks
3 On clicking a recommendation:
Mark text as correctly classi

21. ed and add to corpus
Increase score of the recommended website for future recommendations
4 On clicking a feedback link
5 Mark text as incorrectly classi

22. ed and add to marked corpus
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23. Smart Chat
Results Selecting Algorithm
Figure: Comparison of learning algorithms on our dataset
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24. Smart Chat
Results Feature Selection
Figure: K best features (Log scale) using Chi-Square measure
Top: Linear SVM, middle: Multinomial, bottom: Bernoulli
Multinomial outperforms Bernoulli for large vocabulary
Good accuracy obtained on using 5000 words as features
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25. Smart Chat
Future Scope
Personalization of the feature extraction technique
Clustering of users based on their preferences
Maintaining an activity calendar to remind user of his schedule while
planning tentative work on chat.
Yet to implement the calendar feature in the application
Changing from a Ajax based chat to an XMPP chat server
Integrating with Facebook chat API
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