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How do you deal with cold start problems in your recommender system without compromising user privacy?

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本文章的要点总结

Hybrid recommendation methods:

Combining different recommender systems like collaborative, content-based, and knowledge-based filtering can mitigate cold start issues while minimizing data needs.
Use privacy-preserving techniques:

Implement methods such as anonymization or differential privacy to protect user data while enhancing your recommender system's trustworthiness and compliance.

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1 Hybrid methods

One way to deal with the cold start problem is to combine different types of recommender systems, such as collaborative filtering, content-based filtering, and knowledge-based filtering, to leverage the strengths and compensate the weaknesses of each method. For example, collaborative filtering relies on the similarity and ratings of users and items, but it may not work well for new users or items that have no or few ratings. Content-based filtering uses the features and attributes of users and items, but it may not capture the diversity and serendipity of user preferences. Knowledge-based filtering uses explicit knowledge and rules about users and items, but it may require more user input and interaction. By integrating these methods, you can provide more robust and diverse recommendations that can handle different cold start scenarios and reduce the dependency on user data.

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Medhavi Bhatia

Software Leader | AI, ML, AWS, Azure
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One approach we may have overlooked is how GenAI and vector databases can be used (independently or combined). For example, item descriptions can be created into embedding vectors using an LLM. For any new item the similarity search in the vector db will provide existing item recommendations. For new users, LLMs can use user profile, demographic and most importantly, they can be used to engage a user in a chat that can be used to match recommendations (again using embedding vectors).

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Yunlong Wu
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A common view about users cold start is to guess what users want or what they probably want. Therefore, when a user has a soccer interest, the system should recommend the soccer contents for him/her at the cold start phase. However, this method could bring problems since it misunderstand the intention of the users. For example, if the application has a few soccer contents and their quality is not so good, even the short-term click-rate meets the threshold, the user will find there is no good contents of soccer and finally leave. Therefore, providing what users like is important, showing what we have and we are good at is more important. Users can change their interests easily. For application, changing the contents structure is hard.

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2 Pre-training and transfer learning

Another way to deal with the cold start problem is to use pre-training and transfer learning techniques, which can help you leverage existing data and models from other domains or sources to improve your recommender system. For example, you can use pre-trained models or embeddings that capture general features or patterns of users or items, such as demographics, categories, or genres, and then fine-tune them with your specific data or task. Alternatively, you can use transfer learning techniques, such as domain adaptation or meta-learning, to adapt your model to new domains or tasks with limited or no labeled data. These techniques can help you reduce the data requirements and improve the generalization and adaptation of your recommender system.

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Michael Chen
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I think the main question defines "cold start" as the zero knowledge state of a virgin system. Pre-training and transfer learning imply knowledge storage and/or serialization, in which case "cold start" may refer to how fast it can come online. I recall Josh Wheaton's Dollhouse where, by inserting a chip, a bionic person instantaneously gains new skills and knowledge. I would like to see more researches and experiments towards that goal.

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3 Active learning and feedback

A third way to deal with the cold start problem is to use active learning and feedback techniques, which can help you acquire more relevant and informative data from users or items in an efficient and interactive way. For example, you can use active learning strategies, such as uncertainty sampling, diversity sampling, or expected utility, to select the most valuable users or items to query or recommend, based on the uncertainty, diversity, or utility of their ratings or feedback. Alternatively, you can use feedback techniques, such as explicit ratings, implicit signals, or conversational agents, to collect more data from users or items, based on their preferences, behavior, or interaction. These techniques can help you increase the data quality and quantity and improve the learning and recommendation of your recommender system.

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4 Privacy-preserving techniques

A fourth way to deal with the cold start problem is to use privacy-preserving techniques, which can help you protect the privacy and security of users or items while collecting or using their data for your recommender system. For example, you can use anonymization or pseudonymization techniques, such as encryption, hashing, or masking, to remove or hide the identifiable information of users or items, such as names, emails, or addresses. Alternatively, you can use differential privacy or federated learning techniques, such as adding noise, aggregating data, or distributing computation, to limit or prevent the disclosure of sensitive information of users or items, such as ratings, preferences, or behavior. These techniques can help you balance the trade-off between recommendation accuracy and user privacy and ensure the compliance and trustworthiness of your recommender system.

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Shaurya Uppal

Data Scientist | MS CS, Georgia Tech | AI, Python, SQL, GenAI | Inventor of Ads Personalization RecSys Patent | Makro | InMobi (Glance) | 1mg | Fi
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I have worked with a Neobank where so many Audits happen. Privacy is such sectors is the most important factor. We developers cannot access users’ PII or SPI but data with pattern encryption or a column/feature can directly be accessed by the DS model not developers. There is also a very famous Santander Bank Kaggle competitive where they gave only some numeric features without any insight or view on column names. Just some random looking float values used for classification. Privacy importance over the years has only increased and a special role for Privacy expert Data Scientist will open up ??

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Shaurya Uppal

Data Scientist | MS CS, Georgia Tech | AI, Python, SQL, GenAI | Inventor of Ads Personalization RecSys Patent | Makro | InMobi (Glance) | 1mg | Fi
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With more emphasis to profitability in the ecosystem. When implementing a RecSys model one also has to look into the ROI of the model and how often the RecSys model should retrain. Temporal diversity is a factor that one should look into to figure out frequency of retraining of a RecSys pipeline.

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How do you deal with cold start problems in your recommender system without compromising user privacy?

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1 Hybrid methods

2 Pre-training and transfer learning

3 Active learning and feedback

4 Privacy-preserving techniques

5 Here’s what else to consider

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