Whether the micro-hunting recommender model of last class is reliable or not needs to be evaluated from many aspects. Here are some evaluation points: 1. Platform reputation: To understand the reputation and reputation of the platform, we can evaluate its reliability by looking at user evaluation, media reports and industry recognition.

2. Qualifications of referees: Pay attention to the qualifications and background of micro-hunting referees, and understand their professional knowledge, experience and credibility. An excellent recommender should have knowledge and experience in related fields and be able to provide accurate and valuable recommendations.

3. Data and algorithm: The class recommendation system should be based on scientific data and algorithm to improve the accuracy and reliability of recommendation. Understand how the platform collects, analyzes and utilizes data, and the rationality and effectiveness of its algorithm.

4. User experience: consider the user's experience when using online platform and micro-hunting recommendation mode. A reliable platform should provide convenient and efficient services to meet the needs of users.

5. Risks and safeguard measures: Understand the risks that the platform may face in the recommendation process and evaluate the corresponding safeguard measures it takes. Such as data security, privacy protection, false information prevention and other measures.

To sum up, to determine whether the micro-hunting recommender model is reliable, we need to comprehensively consider many factors such as platform reputation, recommender qualification, data and algorithm, user experience, risk and guarantee. Before making a decision, I suggest you further study and understand what class platform to take, exchange experiences with other users, or consult relevant professionals.