ad recommendation algorithm

Context:
- It can involve analyzing User Data such as Browsing History, Purchase History, and User Preferences.
- It can range from being a Rule-Based Ad Recommendation Algorithm to being an ML-based Ad Recommendation Algorithm.
- It can use Content-Based Filtering methods, recommending ads based on similarity between content of ads and user interests.
- It can involve Contextual Bandit Algorithms, balancing exploration of new ad strategies and exploitation of known high-performing ads.
- It can include Reinforcement Learning approaches, optimizing for long-term engagement and user value.
- It can consider the balance between ad relevance and user experience to prevent ad fatigue.
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Example(s):
- a Collaborative Filtering-based Ad Recommendation Algorithm, which recommend ads based on similarities between users.
- A Rule-Based Ad Recommendation Algorithm that shows ads based on predefined rules like user demographics or time of day.
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Counter-Example(s):
- A Movie Recommendation Algorithm, which suggests movies instead of ads.
- A Search Algorithm, which ranks general search results, not specific to advertisements.
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See: Ad Recommendation System, Ad Recommendation Task, User Data Analysis in Advertising, Ethical Advertising Practices.

References

(Theocharous et al., 2015) ⇒ Georgios Theocharous, Philip S. Thomas, and Mohammad Ghavamzadeh. (2015). “Ad Recommendation Systems for Life-Time Value Optimization.” In: Proceedings of the 24th International Conference on World Wide Web, pp. 1305-1310.
- ABSTRACT: This paper explores various algorithmic strategies in ad recommendation, including supervised learning, contextual bandit algorithms, and reinforcement learning methods. These approaches are evaluated in terms of their ability to optimize for single step (click-through rate) and multi-step (life-time value) objectives, demonstrating the nuanced differences in performance metrics for ad recommendation.