An interactive recommender system pursues two somewhat contradictory goals. On one hand, the system should provide highly relevant recommendations with the best match to the overall user needs. On the other hand, the recommendations should be sufficiently diverse to cover a range of users' possible interests. Such recommendations increase chances that the user finds items that match their context while also informing the system which items are currently most important. In this paper, we present a ranking approach that balances the demands of relevance and coverage. We evaluate the approach on two problems, of advisor and movie recommendations, where the immediate needs of the user are likely to be diverse. Our approach considerably increases chances that the user finds relevant items in the first few steps of the recommendation dialog.

1. Towards Increasing the
Coverage of Interactive
Recommendations
Behnam Rahdari1, Peter Brusilovsky1, Branislav Kveton2
1 University of Pittsburgh, 2 Amazon

2. Interactive Recommender Systems
● Why do we need Human-AI Collaboration in Recommender systems?
○ User ratings alone is incomplete and noisy.
○ We usually don’t know about immediate interest of the user.
● When Human-AI Collaboration is more important?
○ When user have several parallel Interest
○ When user interests have different intensity
○ When we don’t know about these interest!
● How more coverage could help!
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3. What Has Been Done to Solve this?
● Diversification in IR level
○ Improves the coverage
○ Not designed with user in mind (AI alone does the job)
● Context-aware recommendation
○ Considers the context
○ Not designed with user in mind (AI alone does the job)
● Human-AI Collaboration
○ Dialogue-Based: Explicit feedback from the user
○ Critique-Based: Starts with an “Average Best” and improves gradually
○ Interactive Recommenders: Direct manipulation of the user profile
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4. Examples of Interactive Recommender systems
● AI Learns from human
● Short Term:
○ Immediate needs
● Long Term:
○ Overall Preferences
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5. In this Paper
● We propose a greedy approach that generate an optimally balanced list
○ Specifically designed with interactivity in mind
○ Simple, fast and easy to integrate in any ranking algorithm
● In the rest of this presentation:
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Standard
Notation
Problem
with Top-
K
Incorporating
Diversity
Proposed
Solution
Evaluation
Details Results

6. Settings
To formulate our problem, we introduce some notations:
● Set of all Items: I , each item: i ∈ I
● Set of all Topics: J , each item: j ∈ J
● Item-Topic Relevance: s(i, j) ≥ 0
● Topic Preference: wj ≥ 0
● User Model: Topic Preference for all topics of interests
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7. Top-K Ranking Model
● Top-K ranking with multiple topics of interest:
● Total relevance for items I ⊆ I :
● Optimal set of items:
● Why this is problematic?
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w1 = 1 , s(i, j) = (0.5, 0) w2 = 1 - ε , s(i, j) = (0, 0.5)
Top-K with K=4
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8. Incorporating Diversity to Increase Coverage
● To address the problem, we borrow ideas from submodularity:
● We are guaranteed to include at least one item with a very high similarity to
at least one topic with a large weight:
○ For the remainings have no effect!
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w1 = 1 , s(i, j) = (0.5, 0) w2 = 1 - ε , s(i, j) = (0, 0.5)
Top-K with K=4
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9. Proposed Solution
● Proposed objective function:
● Selects items with the highest marginal gain over the previously chosen
items
● Generalization of the cascade model
● It Has been used before to turn down the noise in blogs (El-Arini et al. 2009)
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w1 = 1 , s(i, j) = (0.5, 0) w2 = 1 - ε , s(i, j) = (0, 0.5)
Top-K with K=4
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10. Evaluation
● Two Use Cases:
○ More topics than items: Advisor Recommendations (In House Dataset)
○ More items than topics: Movie Recommendations (MovieLens Dataset)
● User Behaviour Simulation (100-1000 simulations)
○ A set of random topics are realized by user
○ User stops when all topics are covered by at least one above average items
● Satisfactory Index: Where use find the first satisfactory item for each topic j in
their profile.
● Conditions: Topic Size / No. of Simulations / Fixed vs Weighted Topics
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11. Advisor Recommendation
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12. Movie Recommendation
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13. Topic Preference
Advisor Recommendation Movie
Recommendation 13

14. Performance
● Considerable Improvements when there are more items than topics
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15. Discussion and Future Works
● We describe a common problem with top-k ranking model for the
recommendation
● We proposed a solution that increases the coverage of relevant items in a
interactive recommendation setting
● Our solution provides the users with a better opportunity to explore the items
that usually being overlooked due to using a modular top-k rating model
● We Plan to:
○ Study the effectiveness of our approach with real users
○ Investigate other challenges in human-AI collaborations in recommender systems
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16. Thank you
QUESTIONS?
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