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+package com.tzld.piaoquan.recommend.server.service.recall.strategy;
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+
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+import com.tzld.piaoquan.recommend.server.model.Video;
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+import com.tzld.piaoquan.recommend.server.service.filter.FilterParam;
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+import com.tzld.piaoquan.recommend.server.service.filter.FilterResult;
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+import com.tzld.piaoquan.recommend.server.service.filter.FilterService;
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+import com.tzld.piaoquan.recommend.server.service.recall.FilterParamFactory;
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+import com.tzld.piaoquan.recommend.server.service.recall.RecallParam;
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+import com.tzld.piaoquan.recommend.server.service.recall.RecallStrategy;
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+import com.tzld.piaoquan.recommend.server.util.DkElementsUtils;
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+import com.tzld.piaoquan.recommend.server.util.FeatureUtils;
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+import lombok.extern.slf4j.Slf4j;
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+import org.apache.commons.collections4.CollectionUtils;
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+import org.apache.commons.collections4.MapUtils;
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+import org.apache.commons.lang3.StringUtils;
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+import org.apache.commons.lang3.math.NumberUtils;
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+import org.apache.commons.lang3.tuple.Pair;
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+import org.springframework.beans.factory.annotation.Autowired;
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+import org.springframework.beans.factory.annotation.Qualifier;
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+import org.springframework.data.redis.core.RedisTemplate;
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+import org.springframework.stereotype.Component;
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+
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+import java.util.*;
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+import java.util.stream.Collectors;
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+
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+/**
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+ * 视频解构 实质元素 rovn 召回 (用户近期 有效播放 行为 -> dk_elements)
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+ * 触发源相对 YearShareDkElementsRecallStrategy 改为有效播放行为, 其余 Redis 倒排、参数完全一致.
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+ *
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+ * 数据通道差异: share/click 走 a_v_s + a_t_s 配对序列+类型过滤; 有效播放走独立 rp_vid 序列,
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+ * 该序列上游已只保留"有效播放" vid, 无需 type 过滤.
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+ *
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+ * 挑 kw 逻辑: 按 rp_vid 时间序遍历, 每个 vid 摊平多个 element, distinct 取前 topN (30) 个
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+ * -> 一次 multiGet elements_rovn_recall:{kw} 倒排.
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+ *
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+ * 上游 ODPS: alg_recsys_recall_elements_rovn (原始元素 -> top-50 vid + rovn 得分)
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+ * Redis key: elements_rovn_recall:{原始元素}
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+ * value: vid1,vid2,...\tscore1,score2,...
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+ */
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+@Slf4j
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+@Component
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+public class YearValidPlayDkElementsRecallStrategy implements RecallStrategy {
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+
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+ @Autowired
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+ @Qualifier("redisTemplate")
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+ private RedisTemplate<String, String> redisTemplate;
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+
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+ @Autowired
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+ private FilterService filterService;
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+
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+ private final String CLASS_NAME = this.getClass().getSimpleName();
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+
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+ public static final String PUSH_FROM = "recall_user_year_valid_play_dk_elements";
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+ public static final String redisKeyPrefix = "elements_rovn_recall";
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+
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+ /** 用户近期有效播放 vid 序列 key, 上游已只保留有效播放, 无需 type 过滤 */
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+ public static final String VID_SEQ_KEY = "rp_vid";
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+
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+ /** 元素贡献分过滤阈值 (parse 时丢弃 c < 0.8 的 element, 噪声元素不进召回) */
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+ public static final double MIN_CONTRIB_SCORE = 0.8;
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+ /** 摊平后按时间序 distinct 取前 N 个 element 进 Redis 倒排查询 */
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+ public static final int topN = 30;
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+
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+ @Override
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+ public List<Video> recall(RecallParam param) {
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+
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+ List<Video> videosResult = new ArrayList<>();
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+ try {
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+
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+ if (MapUtils.isEmpty(param.getUserNetworkSeqVideoInfoMap())) {
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+ return videosResult;
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+ }
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+
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+ List<Pair<Long, String>> userValidPlayVideoElement = this.parseUserValidPlayVideoAndElements(param.getUserNetworkSeqFeature(), param.getUserNetworkSeqVideoInfoMap());
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+ if (CollectionUtils.isEmpty(userValidPlayVideoElement)) {
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+ return videosResult;
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+ }
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+ // 按用户近期 有效播放 行为时间序遍历, distinct 取前 topN 个高贡献分 element
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+ // (贡献分过滤已在 parseUserValidPlayVideoAndElements 内 c >= MIN_CONTRIB_SCORE 完成)
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+ List<String> allElements = userValidPlayVideoElement.stream()
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+ .map(Pair::getValue)
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+ .filter(StringUtils::isNotBlank)
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+ .distinct()
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+ .limit(topN)
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+ .collect(Collectors.toList());
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+
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+ List<String> keys = this.getRedisKey(allElements);
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+ List<String> values = redisTemplate.opsForValue().multiGet(keys);
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+
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+ // 保留 Redis 倒排的真实 rovn 分 (而非位置分): scoresMap 的 score 会写到 Video.rovScore,
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+ // 粗排截断 coarseMap miss 的 vid 会 fallback 用 Video.rovScore 排序, 真实分更有信号.
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+ Map<Long, Double> scoresMap = recall(param.getVideoId(), values);
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+ List<Long> ids = scoresMap.entrySet().stream()
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+ .sorted(Comparator.comparingDouble((Map.Entry<Long, Double> e) -> e.getValue()).reversed())
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+ .map(Map.Entry::getKey)
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+ .collect(Collectors.toList());
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+
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+ FilterParam filterParam = FilterParamFactory.create(param, ids, pushFrom(), scoresMap);
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+ FilterResult filterResult = filterService.filter(filterParam);
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+ if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
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+ for (Long vid : filterResult.getVideoIds()) {
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+ Video video = new Video();
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+ video.setVideoId(vid);
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+ video.setRovScore(scoresMap.getOrDefault(vid, 0.0));
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+ video.setPushFrom(pushFrom());
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+ videosResult.add(video);
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+ }
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+ }
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+ } catch (Exception e) {
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+ log.error("recall is wrong in {}, error={}", CLASS_NAME, e);
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+ }
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+
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+ return videosResult;
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+ }
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+
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+ /**
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+ * 摊平: 每个有效播放 vid 一般有多个 dk_element, 输出 (vid, element) pair 序列, 按 vid 时间序保留.
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+ * rp_vid 序列上游已是"有效播放过滤后"的 vid 列表, 不再做 type 过滤.
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+ */
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+ private List<Pair<Long, String>> parseUserValidPlayVideoAndElements(Map<String, String> userNetworkSeqFeature, Map<Long, Map<String, String>> userNetworkSeqVideoInfoMap) {
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+ List<Pair<Long, String>> result = new ArrayList<>();
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+ List<String> rpVidSeq = FeatureUtils.extractVidsFromUserNetworkSeqFeature(userNetworkSeqFeature, VID_SEQ_KEY);
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+ if (CollectionUtils.isEmpty(rpVidSeq)) {
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+ return result;
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+ }
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+
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+ for (String vidStr : rpVidSeq) {
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+ long videoIdL = NumberUtils.toLong(vidStr, -1);
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+ if (videoIdL <= 0) {
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+ continue;
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+ }
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+
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+ Map<String, String> videoBaseInfo = userNetworkSeqVideoInfoMap.getOrDefault(videoIdL, new HashMap<>());
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+ String dkElementsStr = videoBaseInfo.get("dk_elements");
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+ if (StringUtils.isBlank(dkElementsStr)) {
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+ continue;
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+ }
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+ List<String> kws = DkElementsUtils.parseElementKws(dkElementsStr, MIN_CONTRIB_SCORE);
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+ for (String kw : kws) {
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+ result.add(Pair.of(videoIdL, kw));
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+ }
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+ }
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+ return result;
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+ }
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+
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+ private List<String> getRedisKey(List<String> elementList) {
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+ List<String> keys = new ArrayList<>();
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+ for (String element : elementList) {
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+ keys.add(String.format("%s:%s", redisKeyPrefix, element));
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+ }
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+ return keys;
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+ }
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+
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+ /**
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+ * 解析 multiGet 拿到的 N 个 Redis value, 拼成 vid -> 真实 score map.
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+ * value 格式: vid1,vid2,...\tscore1,score2,... (rovn 真实分)
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+ * 同 vid 在多个 element 倒排里出现时, 取 max score (跟 AbstractRedisRecallStrategy 一致).
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+ */
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+ private Map<Long, Double> recall(Long headVid, List<String> values) {
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+ Map<Long, Double> scoresMap = new HashMap<>();
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+ if (CollectionUtils.isEmpty(values)) {
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+ return scoresMap;
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+ }
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+ for (String value : values) {
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+ if (StringUtils.isBlank(value)) {
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+ continue;
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+ }
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+ String[] cells = value.split("\t");
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+ if (cells.length != 2) {
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+ continue;
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+ }
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+ List<Long> ids;
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+ List<Double> scores;
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+ try {
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+ ids = Arrays.stream(cells[0].split(",")).map(Long::valueOf).collect(Collectors.toList());
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+ scores = Arrays.stream(cells[1].split(",")).map(Double::valueOf).collect(Collectors.toList());
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+ } catch (NumberFormatException nfe) {
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+ continue;
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+ }
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+ if (ids.isEmpty() || ids.size() != scores.size()) {
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+ continue;
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+ }
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+ for (int i = 0; i < ids.size(); i++) {
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+ long id = ids.get(i);
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+ if (headVid != null && headVid == id) {
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+ continue;
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+ }
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+ scoresMap.merge(id, scores.get(i), Math::max);
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+ }
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+ }
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+ return scoresMap;
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+ }
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+
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+ @Override
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+ public String pushFrom() {
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+ return PUSH_FROM;
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+ }
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+}
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