What are the potential drawbacks or limitations of the uniform and Zipf distributions used to model the recommendation-induced preference distributions
Drawbacks of Uniform Distribution:
Lack of Personalization: Uniform distribution treats all recommended items equally, regardless of individual user preferences, leading to a lack of personalization in recommendations.
Limited Relevance: Uniform distribution may not capture the varying degrees of relevance that different items hold for users, potentially resulting in suboptimal recommendations.
Ignoring User Heterogeneity: Users have diverse preferences, and a uniform distribution may overlook this heterogeneity, failing to tailor recommendations to individual needs effectively.
Limitations of Zipf Distribution:
Complexity: Zipf distribution models can be complex and may require additional parameters to accurately capture user preferences, making them computationally intensive.
Assumption of Positional Influence: Zipf distribution assumes that the position of an item in the recommendation list directly influences its selection probability, which may not always hold true in real-world scenarios.
Limited Flexibility: Zipf distribution may not be able to capture nuanced user behaviors and preferences that do not align with a strict positional influence model, limiting its adaptability in diverse recommendation settings.