Embodiments of this application disclose a recommendation method, comprising: determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each branch node of a content tree, a probability that the user selects each branch node on an nth level of the content tree; and recommending, to the user according to the probability that the user selects each branch node on the nth level of the content tree, a content category corresponding to at least one branch node on the nth level of the content tree. This application further discloses another recommendation method and recommendation apparatus. According to the recommendation method and apparatus in the embodiments of this application, a probability that a user selects each node on a specific level in a tree structure of to-be-recommended contents can be determined according to a hidden variable characteristic parameter of the user, and a recommendation is given based on the probability, which overcomes a problem in the prior art that a need of a user for customization is overlooked, so that a customized recommendation can be given to a user more accurately.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A recommendation method, comprising: determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each branch node of a content tree, a probability that the user selects each branch node on an nth level of the content tree, wherein one branch node of the content tree corresponds to one content category, and n is a natural number greater than 1; and recommending, to the user according to the probability that the user selects each branch node on the nth level of the content tree, a content category corresponding to at least one branch node on the nth level of the content tree. 2. The method of claim 1, wherein the determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each branch node of a content tree, a probability that the user selects each branch node on an nth level of the content tree comprises: calculating an affinity between the user and each branch node on the nth level of the content tree according to the hidden variable characteristic parameter of the user and a hidden variable characteristic parameter of each branch node on the nth level of the content tree; and determining, according to the affinity between the user and each branch node on the nth level of the content tree, the probability that the user selects each branch node on the nth level of the content tree. 3. The method of claim 2, wherein the affinity between the user and each branch node on the nth level of the content tree is a dot product of the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each branch node on the nth level of the content tree. 4. The method of claim 1, wherein the method further comprises: pre-determining the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each branch node of the content tree. 5. The method of claim 1, wherein the content tree is an application tree, and the content category is an APP category; or the content tree is a commodity tree, and the content category is a commodity category; or the content tree is a search result tree, and the content category is a search result category. 6. A recommendation method, comprising: determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each leaf node of a content tree, a probability that the user selects each leaf node of the content tree, wherein one leaf node of the content tree corresponds to one content; and recommending, to the user according to the probability that the user selects each leaf node of the content tree, a content corresponding to at least one leaf node of the content tree. 7. The method of claim 6, wherein the determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each leaf node of a content tree, a probability that the user selects each leaf node of the content tree comprises: calculating an affinity between the user and each leaf node of the content tree according to the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each leaf node of the content tree; and determining, according to the affinity between the user and each leaf node of the content tree, the probability that the user selects each leaf node of the content tree. 8. The method of claim 7, wherein the affinity between the user and each leaf node of the content tree is a dot product of the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each leaf node of the content tree. 9. The method of claim 6, wherein the method further comprises: pre-determining the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each leaf node of the content tree. 10. The method of claim 6, wherein the content tree is an APP tree, and the content is an APP; or the content tree is a commodity tree, and the content is a commodity; or the content tree is a search result tree, and the content is a search result. 11. A recommendation apparatus, comprising: a probability determining module, configured to determine, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each branch node of a content tree, a probability that the user selects each branch node on an nth level of the content tree, wherein one branch node of the content tree corresponds to one content category, and n is a natural number greater than 1; and a recommendation module, configured to recommend, to the user according to the probability that the user selects each branch node on the nth level of the content tree, a content category corresponding to at least one branch node on the nth level of the content tree. 12. The apparatus of claim 11, wherein the probability determining module comprises: an affinity determining unit, configured to calculate an affinity between the user and each branch node on the nth level of the content tree according to the hidden variable characteristic parameter of the user and a hidden variable characteristic parameter of each branch node on the nth level of the content tree; and a probability determining unit, configured to determine, according to the affinity between the user and each branch node on the nth level of the content tree, the probability that the user selects each branch node on the nth level of the content tree. 13. The apparatus of claim 11, wherein the apparatus further comprises: a parameter determining module, configured to pre-determine the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each branch node of the content tree. 14. A recommendation apparatus, comprising: a probability determining module, configured to determine, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each leaf node of a content tree, a probability that the user selects each leaf node of the content tree, wherein one leaf node of the content tree corresponds to one content; and a recommendation module, configured to recommend, to the user according to the probability that the user selects each leaf node of the content tree, a content corresponding to at least one leaf node of the content tree. 15. The apparatus of claim 14, wherein the probability determining module comprises: an affinity determining unit, configured to calculate an affinity between the user and each leaf node of the content tree according to the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each leaf node of the content tree; and a probability determining unit, configured to determine, according to the affinity between the user and each leaf node of the content tree, the probability that the user selects each leaf node of the content tree. 16. The apparatus of claim 14, wherein the apparatus further comprises: a parameter determining module, configured to pre-determine the hidden variable characteristic parameter of the user and the hidden variable characteristic parameter of each leaf node of the content tree. 17. A recommendation apparatus, comprising: a memory and a processor, wherein the memory is configured to store an instruction; and the processor is configured to execute the instruction, to perform the following steps: determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each branch node of a content tree, a probability that the user selects each branch node on an nth level of the content tree, wherein one branch node of the content tree corresponds to one content category, and n is a natural number greater than 1; and recommending, to the user according to the probability that the user selects each branch node on the nth level of the content tree, a content category corresponding to at least one branch node on the nth level of the content tree. 18. A recommendation apparatus, comprising: a memory and a processor, wherein the memory is configured to store an instruction; and the processor is configured to execute the instruction, to perform the following steps: determining, according to a hidden variable characteristic parameter of a user and a hidden variable characteristic parameter of each leaf node of a content tree, a probability that the user selects each leaf node of the content tree, wherein one leaf node of the content tree corresponds to one content; and recommending, to the user according to the probability that the user selects each leaf node of the content tree, a content corresponding to at least one leaf node of the content tree.